Local usage of electronic tokens in a transaction processing system

ABSTRACT

A method for the use of electronic transactional tokens includes: generating a plurality of transactional tokens including a first token; associating each of the transactional tokens with a plurality of users, the first token associated with a first user; monitoring usage of the transactional tokens in a plurality of transactions in a local region or network; and in response to receiving transactional data for the first transaction, updating the first token from a first state to a second state (e.g., to provide a benefit when the token is applied in the local region or network). The tokens may be generated and monitored using a token processing system, which uses transaction data received by a transaction handler that is handling transaction processing for the transactions.

RELATED APPLICATIONS

The present application is a continuation of U.S. patent applicationSer. No. 16/442,996, filed Jun. 17, 2019, which is a continuation ofU.S. patent application Ser. No. 13/485,645, now U.S. Pat. No.10,380,585, filed May 31, 2012, which claims priority to U.S.Provisional Patent Application Ser. No. 61/492,768, filed Jun. 2, 2011,entitled “Local Usage of Electronic Tokens in a Transaction ProcessingSystem”, by Harris et al., the disclosures of which are incorporatedherein by reference in their entireties.

FIELD OF THE TECHNOLOGY

At least some embodiments of the present disclosure relate to theprocessing of local transactions, such as payments made via paymentcards such as credit cards, debit cards, prepaid cards, etc., and themanaging of electronic tokens associated with various entities involvedin these local transactions.

BACKGROUND

Millions of transactions occur daily through the use of payment cards,such as credit cards, debit cards, prepaid cards, etc. Correspondingrecords of the transactions are recorded in databases for settlement andfinancial recordkeeping (e.g., to meet the requirements of governmentregulations). Such data can be analyzed for trends, statistics, andother analyses. Sometimes, such data are mined for specific advertisinggoals, such as to provide targeted offers to account holders, asdescribed in PCT Pub. No. WO 2008/067543 A2, published on Jun. 5, 2008and entitled “Techniques for Targeted Offers,” which is herebyincorporated herein by reference.

In some cases, coupons (e.g., physical coupons distributed in publishedmagazines with accompanying advertisements) may be used in some of thesetransactions. These coupons are typically targeted to individualconsumers and offer a one-time discount for a single purchase of a goodor service. However, consumers often view such coupons as being mundaneor dull, and generating significant consumer interest in the coupons isfrequently challenging to product marketers.

In other cases, a local community engaging in transactions desires topromote businesses that are located within a defined city or other localregion. This is due in part to a growing interest in promoting theconsumption of locally-produced products. However, it continues to bedifficult for a community to retain income that is earned locally byhaving or encouraging that income to be spent locally. Some communitieshave experimented with the use of local currencies to accomplish thisresult. However, the use of such currencies may raise legal issues andalso may increase the risk of inducing localized inflation or deflation.

BRIEF DESCRIPTION OF THE DRAWINGS

The embodiments are illustrated by way of example and not limitation inthe figures of the accompanying drawings in which like referencesindicate similar elements.

FIG. 1 illustrates a transactional flow involving multiple transactionsand entities, including transactions within a local region or network,according to one embodiment.

FIG. 2 illustrates token data record according to one embodiment.

FIG. 3 illustrates a trophy data record according to one embodiment.

FIG. 4 illustrates a token processing infrastructure according to oneembodiment.

FIG. 5 illustrates a system to provide services based on transactiondata according to one embodiment.

FIG. 6 illustrates the generation of an aggregated spending profileaccording to one embodiment.

FIG. 7 shows a method to generate an aggregated spending profileaccording to one embodiment.

FIG. 8 shows a system to provide information based on transaction dataaccording to one embodiment.

FIG. 9 illustrates a transaction terminal according to one embodiment.

FIG. 10 illustrates an account identifying device according to oneembodiment.

FIG. 11 illustrates a data processing system according to oneembodiment.

FIG. 12 shows the structure of account data for providing loyaltyprograms according to one embodiment.

FIG. 13 shows a system to provide real-time messages according to oneembodiment.

FIG. 14 shows a method to provide real-time messages according to oneembodiment.

DETAILED DESCRIPTION Introduction

At least some embodiments of the present disclosure relate to theprocessing of transactions, such as payments made via payment cards suchas credit cards, debit cards, prepaid cards, etc., and the managing ofelectronic tokens associated with various entities (e.g., users of atransactional token processing system) involved in these transactions(e.g., using transaction data obtained during this processing, such asby a transaction handler). In one example, each of the tokens provides abenefit (e.g., a discount of 30%) to a consumer when making a purchaseusing a credit card or other payment card. In another example, thetokens are limited to usage in a defined local region or network (e.g.,having a common electronic network address or domain).

As used herein, “transactional token” refers generally to an electronictoken that provides a benefit to an entity (e.g., when the token isapplied to a transaction involving the entity, or when the token isaccumulated with other tokens by a single user or entity). Thesetransactional tokens may be generated and issued to various entities(e.g., consumers and merchants) by a token processing system (discussedin more detail below). In some embodiments, the transactional token mayprovide a benefit that is the same or similar to that of a coupon (e.g.,a 10% off coupon), but transactional tokens are not limited to usesolely as a coupon (e.g., transactional tokens may have other attributesand characteristics as described below). Entities to which tokens areissued may be users of the token processing system (e.g., a user thataccesses the system via a user device or user terminal). These users mayaccess information about their tokens, for example, by logging onto thissystem. When the user is merchant, the user may specify the particulargoods or services and conditions under which a token may be used in atransaction involving the merchant's goods or services.

In one embodiment, the token processing system maintains data for thetokens of each user (e.g., a unique identifier for each token that isassociated with that user). The token processing system may, forexample, maintain data regarding various attributes for each token andalso transaction histories associated with each token. For example, afirst attribute for a token may be a discount to be applied to apurchase. Another attribute may be a loyalty reward point allocationthat is associated with use of the token in a transaction. As a token isused, transaction data associated with its use may be stored as a tokentransaction history that remains permanently associated with the token.

In one embodiment, a method for the use of transactional tokenscomprises the following steps: (i) generating transactional tokens(e.g., including a first token to offer a discount for making a purchasefrom a merchant); (ii) associating each of the transactional tokens withone of a group of users (e.g., the first token is associated with afirst user); (iii) monitoring usage of the transactional tokens in amultitude of transactions by the users; and (iv) responsive to themonitoring, updating the first token from a first state (e.g., adiscount of 30%) to a second state (e.g., the discount has been applied,but the purchaser now holds a trophy due to making the purchase). Thetokens may be generated and monitored using a token processing system,which may interact with and use transaction data received by atransaction handler that is processing each of the transactions.

In one embodiment, the token processing system permits users to transfertokens from one user or entity to another. These entities may be usersof the token processing system, but in other embodiments, an entity thatis not a user may receive the token.

In another embodiment, a transactional token may be used or applied tomore than one transaction. The token may change state as each of thesetransaction occurs (i.e., as the token is applied to each of thetransactions by the user that is currently holding the token). The tokenmay further change state based on other data collected and/or analyzedby the token processing system (e.g., a change in merchant inventory, orpurchases by other consumers of a particular product for which adiscount has been offered). A token that changes state in this manner issometimes described herein as an evolvable token. For example, the tokenmay evolve based on consumer or merchant user interactions (e.g., amerchant request) with the token processing system.

There is a large variety of ways in which such a token may evolve. Forexample, the token may be a coupon with a discount for a product thatchanges based on current economic conditions or the remaining inventoryof a merchant that is selling the product referenced in the coupon.Other examples of evolvable tokens (some for which the transactionaltoken has an attribute of a coupon) include the following: depreciatingor appreciating coupons; user ability to combine its various usercoupons to generate new coupons; user ability to combine coupons ofother users to create new coupons (e.g., by transfer of tokens fromseveral users to a single recipient user); transactional tokens thatgrow in a game-like fashion to have new attributes and value; allowingusers to share and transfer tokens amongst themselves; providingdetailed reports to users or others on usage of tokens; and providingspecial loyalty rewards to frequent coupon users.

In another example, a user's token usage is tracked so that the user mayaccumulate a digital trophy collection (e.g., a set of electronictransactional tokens each having an attribute of a trophy). Also,tracking of this token usage enables coupons that change state based onthe user's holding or acquiring other coupons, or based on the user'sprior coupon usage.

FIG. 1 illustrates a transactional flow 100 involving multipletransactions (Transaction₁ and Transaction₂) and entities (user 102,merchant 104, and supplier 106) according to one embodiment. Here, user102 may possess one or more transactional tokens (e.g., that werepreviously generated and that are managed by a token processing systemas discussed below). Examples of such tokens may include Token₁ andToken₂ (e.g., each token may provide a percentage discount on apurchase). Token₁ has a first state (State_(A)) prior to being appliedor used in Transaction₁. After being applied to Transaction₁, Token₁changes to a second state (State_(B)), for example as updated in amemory of the token processing system.

User 102 may own or otherwise be associated with a user device 108(e.g., a mobile device). User device 108 may store information (e.g., aunique identifier that identifies the tokens that the user 102possesses). A transaction history 110 is associated with user 102 andits prior purchase history. Transaction history 110 may be stored in amemory of user device 108 and/or in a memory of the token processingsystem (or alternatively stored on yet another computing device orserver).

Account information 112 is associated with user 102. Account information112 may be, for example, information regarding a credit card or debitcard account of the user, and account information 112 may be originallygenerated by an issuer. This credit or debit card may be used to makepurchases in Transaction₁ and/or Transaction₂. Processing of atransaction using this credit or debit card may be handled by atransaction handler.

Account information 112 may be uniquely associated with user 102 by anaccount identifier 114 that is part of account information 112. Accountinformation 112 may be stored on one or more of the following: userdevice 108, a computing device of the issuer, a computing device of atransaction handler, and the token processing system discussed herein.

In one embodiment, merchant 104 originally requests the generation ofToken₁ as a user of the token processing system (e.g., via logging onelectronically by a user terminal or device). User 102 may have acquiredToken₁ as part of a marketing promotion (electronic or non-electronic)by merchant 104, in which a discount on purchase of a good wasadvertised. Transaction₁ may be, for example, for the purchase of a goodfrom merchant 104. User 102 may apply Token₁ to obtain a discount onthis purchase, and may make the purchase using a credit card or otherpayment card. The purchase may be processed by a transaction handlerthat passes the associated transaction data to the token processingsystem. The application of the Token₁ to the transaction may beimplemented by providing the unique identifier of Token₁ to thetransaction handler, which may communicate with the token processingsystem. The transaction handler applies a discount to the purchase sothat the user gets the benefit of the application of Token₁.

After Transaction₁ is completed, user 102 retains possession of Token₁,but Token₁ no longer has the attribute of providing a discount for anyfurther purchase. This is reflected and implemented by changing thestate of Token₁ to a new state, State_(B), which is a post-purchasingstate for which no discount is provided to the user. User device 108 andthe token processing system may be updated to reflect this new state ofToken₁. Also, the transaction data from Transaction₁ may be sent to thetoken processing system to update a transaction history that ismaintained for Token₁ (along with other tokens of user 102), asdiscussed further below.

In one embodiment, from the perspective of user 102, Token₁ will be thesame token after Transaction₁, as discussed above, and have the sameunique identifier following the transaction. However, from theperspective of the token processing system, there will be two uniqueidentifiers. The first unique identifier is the parent identifier (ID),which does not change. This may be, for example, the parent ID assignedto the initial Token₁ (e.g., assigned at generation of the token).

The second unique identifier links to the first unique ID and representsthe current state of Token₁ (e.g., State_(A) or State_(B)). As Token₁changes state, a new transactional token (having the second unique ID)is generated and is linked back to the first unique ID (i.e., the parentID). In this manner, a full history of the token's evolution (e.g.,through multiple transactions or other network events) can be stored ina memory of the token processing system.

Although State_(B) of Token₁ provides no further discount, in someembodiments, the user is provided a trophy that reflects the completionof the purchase by user 102 in Transaction₁. The collection of varioustrophies by user 102 may be desirable for user 102 for entertainment orother personal reasons, and further may be used to reflect or recordprogress towards completion or vesting of a new benefit for user 102that may be applied to future transactions. This benefit may be afinancial or other commercial benefit (e.g., a user may collect 10trophies and then exchange these trophies for a new transactional token,according to rules handled by the token processing system).

In one embodiment, user 102 may collect trophies in various ways, all asmanaged by the token processing system. For example, the trophies may beimplemented as transactional tokens having the attribute or state ofbeing a trophy, but without having any attribute that provides adiscount or other benefit in a future transaction. However, a trophy mayhave an attribute such that the collection of two or more trophies doesprovide a transactional benefit as managed by the token processingsystem, or that the trophies may be exchanged for a transactional tokenthat has the attribute of a coupon for a future purchase.

Examples of trophies held by user 102 are Trophy_(A) and Trophy_(B).Token₁ in State_(B) above was described as being in the state of atrophy. In an alternative embodiment, after completion of Transaction₁,user 102 may be provided with Trophy_(A) rather than retain Token₁ (inthis case Token₁ may simply expire). Alternatively, user 102 may acquireTrophy_(A) in addition to retaining Token₁ in State_(B). Trophy_(B) may,for example, have been provided to user 102 via a transfer from anotheruser (e.g., a friend or a person in a social network of user 102, asdefined on a social network website or server) of the token processingsystem.

In an alternative embodiment (using a cascading approach), rather thanstaying with user 102 after a transaction is completed, a transactionaltoken may transfer (e.g., automatically via action of the tokenprocessing system upon receiving transaction data) from user 102 tomerchant 104. In such a cascading system, a Token₂ is transferred tomerchant 104 after the Transaction₁, and merchant 104 is then holdingToken₂. The possession of Token₂ by merchant 104 will be updated in amemory of the token processing system. The transfer of tokens from oneuser to another user may be used in various embodiments related to usageof transactional tokens in a local region or network, as discussedfurther below.

Prior to Transaction₁, Token₂ has a Staten (e.g., similar to that asdiscussed above with respect to Token₁). After Transaction₁, the stateof Token₂ is changed to that of providing a discount for a futuretransaction (e.g., Transaction₂) by merchant 104 (e.g., this state is anew state, State_(B), as illustrated). In this cascading system, user102 may also get a trophy (e.g., Trophy_(B)), but the trophy is a newtransactional token (having the attribute of a trophy) generated by thetoken processing system (a new token must be generated since user 102 nolonger holds the Token₂).

Token₂ in State_(B) may, for example, provide merchant 104 with adiscount for a purchase from supplier 106. After Transaction₂ iscompleted (e.g., with transaction processing and data handling similarlyhandled as discussed above for Transaction₁), Token₂ is transferred tosupplier 106 and has a new state, State_(C), that may provide atransactional benefit to supplier 106 for a future transaction. Aftereach transaction, the token processing system updates a memory toreflect the entity that is currently possessing Token₂. The cascadingprocess described above may be repeated for yet further transactionswith other new entities and/or entities that were previously in thetransactional flow 100.

Various Token System Embodiments

As was discussed above, a token processing system may generatetransactional tokens and associate each token with one or more users.The system may monitor usage of the tokens in one or more transactionsby the users (e.g., user 102 and merchant 104). In response to datareceived from this monitoring, the system may update a given token froma first state (e.g., State_(A) of Token₁ or Token₂) to a second state(e.g., State_(B) of Token₁ or Token₂).

Various embodiments of this token processing system are now herediscussed. In a first embodiment, the monitoring includes receivingtransaction data from a first transaction (e.g., Transaction₁) of afirst user (e.g., user 102), the first token being applied to the firsttransaction. In another embodiment, this first transaction is betweenthe first user and a second user (e.g., merchant 104), and the methodfurther comprises responsive to applying the first token to the firsttransaction, transferring the first token to the second user. The secondstate corresponds to a transactional benefit for a future transaction ofthe second user.

In one embodiment, the transactions are for one or more purchases from amerchant by users other than the first user (e.g., users in a socialnetwork or other defined network or group including the first user), thefirst state corresponds to a discount for a future purchase of the firstuser, and the second state corresponds to a change in the discount forthe future purchase, the change corresponding to the purchases by theseother users (e.g., the change may be an increase in the discount basedon a dollar amount of purchases being below a predetermined or targetedthreshold). In another embodiment, the purchases may be by persons thatare not users of the token processing system.

In one embodiment, the first state corresponds to a discount for apurchase from a first merchant (e.g., merchant 104), and the secondstate corresponds to a discount for a purchase from a second merchant(e.g., supplier 106).

In one embodiment, the first token comprises a plurality of attributesincluding a first attribute, and when the first token is in the secondstate, the first attribute provides a percentage reduction in price fora future transaction of the first user. Also, the method furthercomprises sending data regarding the second state to a mobile device(e.g., user device 108) of the first user.

In one embodiment, the first token comprises a plurality of attributesincluding a first attribute representing a trophy (e.g., an attribute ofTrophy_(A) or of Tokens in State_(B)), and the method further comprises,responsive to the first user collecting a predetermined number oftrophies including the trophy, awarding a transactional token to thefirst user (e.g., a new token is generated and associated with the firstuser).

In one embodiment, the method further comprises associating a respectivetransaction history with each of the transactional tokens (e.g., thishistory may be stored in a memory accessible by the token processingsystem), and updating the respective transaction history based ontransactions to which the respective token has been applied.

In one embodiment, the method further comprises, responsive to receivinga request from a merchant, updating the first token to a different state(e.g., the merchant may log in to the token processing system as a userto change the state of the token to correspond to different set of rulesfor obtaining a transactional benefit from application of the token to atransaction). In another embodiment, the method further comprises,responsive to receiving a request from a second user, updating the firsttoken to a different state (e.g., a second user may log into the tokenprocessing system and provide input or a request to change the state ofthe first token).

In one embodiment, the users include a second user, the method furthercomprising responsive to receiving a request from the first user,transferring the first token to the second user. For example, user 102may transfer its Tokens and/or Trophy_(A) to another user of the tokenprocessing system. In one embodiment, the method further comprisesadding an attribute to the first token based on a history of priortransactions of the first user.

In one embodiment, the method further comprises defining a network(e.g., an online grouping of user devices, user accounts, or otherobjects or sources of data that are treated as a group or class), andassociating the transactional tokens with the network. In anotherembodiment, the method further comprises associating a number of userdevices with the network, and receiving transaction data from the userdevices for transactions to which the transactional tokens are applied.

In one embodiment, the monitoring of the usage of the tokens furtherincludes, responsive to receiving transaction data for two or more usersof the plurality of users other than the first user, updating the firsttoken to a different state based on purchases of the two or more users.For example, transaction data received from the purchases by a group ofusers (e.g., a two-level deep social network of the user that isassociated with the token processing system) may be analyzed and used tochange the percentage discount offered in a transactional token.

In one embodiment, the method further comprises collecting dataassociated with the users, correlating the data to identify a newtransactional opportunity (e.g., identifying a product of expectedpurchasing interest for a target group of persons or entities),generating new transactional tokens each providing a transactionalbenefit for the new transactional opportunity, and sending the newtransactional tokens to new users and/or to a target group of persons orentities.

In one embodiment, the receiving of the transaction data by the tokenprocessing system includes receiving the transaction data by atransaction handler configured to receive from acquirer processorsauthorization requests for payments to be made by issuer processorsaccording to account identifiers of users, the issuer processors to makethe payments on behalf of users, and the acquirer processors to receivethe payments on behalf of merchants.

Token Processing System

FIG. 2 illustrates token data record 200 according to one embodiment. Asmentioned above, the token processing system may maintain data regardingvarious attributes 204 for each transactional token. For example, oneattribute may be a discount to be applied to a purchase. A differentattribute may be a trophy or a loyalty reward point allocation. As atoken is used, transaction data associated with its use may be stored astoken transaction history 206, which may be stored in a memory ordatabase.

Token data record 200 includes unique identifier 202 (i.e., the firstunique identifier or parent ID created and associated with the tokenwhen it is first generated). Second and subsequent unique identifiers orlinks 208 that are linked to the parent ID, as discussed above, are usedto define the current state of the token. Each link 208 points to adefinition of the current state and its attributes 204.

As mentioned above, a token may be associated with one or more networks.Token data record 200 may include network connection weights 210 toindicate the strength of association or connection of this particulartoken with a given one or more of these networks.

In one embodiment, networks may be further organized into network groupsfor analysis (e.g., of transactions of persons in the network group). Anetwork group is a well-defined or abstract grouping of severalnetworks. This grouping defines a higher order structure to thenetworks. An example of a network group is the set of all people using asocial website, and a network within that group is a set of all peopledirectly connected to a particular person in that social website. Theconnection between a network and a network group also can indicatestrength, which may be stored as one of network connection weights 210.

FIG. 3 illustrates a trophy data record 300 according to one embodiment.Data record 300 provides data and history for prior purchases associatedwith a uniquely identified trophy (e.g., Token₁ in State_(B), orTrophy_(B), each as discussed above). This data may include transactiondata 302 for a single purchase (e.g., Transaction₁) that led to theexistence of the trophy. This data may include the execution date 304 ofthe transaction and the purchase price 306 for the good or servicepurchased. In one embodiment, trophy data record 300 is incorporatedinto token data record 200.

FIG. 4 illustrates a token processing infrastructure 400 according toone embodiment, including a token processing system 402, suitable forimplementing the systems and methods discussed above. Token processingsystem 402 generates new tokens 412 and new trophies 414. Data for newand existing tokens (e.g., Tokens A1, B1, and C1) is stored as tokendata 426, and each token is assigned a token identifier 428, which isassociated with a current user (e.g., User A, B, or C, etc.). As a tokenmay be transferred by a user or due to some other action or event, thecurrent user of the token is updated in token data 426. Token identifier428 may be generated, for example, as a hash of data (e.g., accountnumber, etc.) associated with a user. Other methods may be used.

As discussed above, each of the tokens managed by token processingsystem 402 may be associated with a network 430 (or alternativelynetwork group). These associations may be stored as part of token data426.

In one embodiment, networks are a container for the tokens (a network issometimes referred to herein as a container class or a container).Networks can be defined abstractly (e.g., people who are likely to buyshoes in the next month) or physical (e.g., all devices that share acertain IP address). The connection or association between a network anda token has a weight (e.g., one of network connection weights 210). Thisweight may indicate a strength of the relationship. For example, theconnection weight between tokens and networks could indicate a degree ofassociation, affiliation, or social unity or community, or a probabilitythat the relationship is valid, or a propensity towards the behaviorused as a basis for defining the network. For example, a value of oneindicates a direct match to the network. The foregoing may be generallydescribed in that networks are containers for data objects associatedwith a set of tokens. In some cases, a network may be viewed as acontainer that links multiple tokens (i.e., the tokens are associatedwith the container).

In one embodiment, token processing system 402 includes a collectorclass and a number of collectors 404. The collector is a process thatcreates tokens (e.g., tokens 412) and networks (e.g., network 430) usingpredefined rules and functions. The collector may be implemented as anadaptable engine that populates the entities, defines the networkrelationships, defines how tokens are to evolve (change state), andcontrols how tokens interact with data and or events received from orassociated with the network.

The collector class is the generic definition, while a specificcollector (e.g., a home address collector) is specific to a certain datasource (e.g., a social networking website). These collectors are XMLconfigurable and hold the meta data definitions, models and dataintegrity rules.

A collector class may be a scoring model that builds statisticalrelationships between entities and networks and/or or creates directconnections (e.g., a connection between a person and a home address).One or more of a variety of known scoring models may be used to buildthese relationships. In general, the collectors build the data objects.A collector group is a higher order definition of collectors such as,for example, a group of social websites versus a single, particularsocial website.

Statistics 424 are stored in a memory or data store accessible to tokenprocessing system 402. Statistics 424 includes data used to build thestatistical relationships (e.g., this data may be collected from userdevices of users during transactions, or from third-party data sourcessuch as demographic, business, or financial data). These relationshipsmay be built in either real-time as transactions occur and/or in a batchmode. In one embodiment, both real-time and batch processing is done.

Account information 416 may be maintained in a data store (accessible totoken processing system 402) and includes user data (e.g., accountinformation 112). Account information 416 includes an account identifier420 for each user, transaction history 418 for storing data from priortransactions (e.g., both for transactions to which tokens are appliedand for other transactions), and user device information 422 toassociate each user with a physical device of that user (e.g., a paymentcard or mobile device used by the user in a transaction). Tokenprocessing system 402 also includes analytics engine 406, identifierengine 408, and reporting module 410.

Exemplary Implementations

Various exemplary implementations and variations are now discussedbelow. These implementations are not to be interpreted as limiting thegenerality of the foregoing systems and methods.

In one example, an electronic or digital coupon is implemented as atransactional token that builds complex networks. The coupon evolves inan interconnected environment having a large number of devices connectedto a common network (i.e., devices associated with a defined network ornetwork group). This evolvable token starts with a 30% discount, thenchanges state to provide a benefit when used with particular type orprovider of a web service, and then changes state again to yet someother benefit for the holder of the token. The token has a unique IDthat is associated after processing with a container group (the IDassociates the token with the container group and the token processingsystem stores this association).

In another example, a trophy has a different value depending on whatperson or merchant the trophy is currently associated with (i.e., thetrophy changes state to have different attributes or benefits as it istransferred from one entity to another). The trophy has a differentvalue depending on the person or entity that is holding the trophy.

In one example, the token processing system may use statistics andtransaction data to determine persons that have not yet purchased a goodor service. The value of a token in possession of such a person isincreased relative to a person who frequently makes purchases from thesame merchant.

In one example, a traditional coupon (which is a form of external tokennot yet entered into the system) may be entered into the tokenprocessing system and handled as a new token within the system. Forexample, the coupon may have an identifying bar code or square that maybe scanned (e.g., using a mobile device camera) and used to obtain dataneeded to build the new token. Each such traditional coupon will have aunique identifier when generated (e.g., coupons typically have an IDnumbering associated with them, for example, when they are provided inmagazines).

For example, the above coupon may be entered into a user network that isassociated with an iPhone or iPad mobile device. The coupon isassociated with this network and is also associated with an ID for thismobile device. The mobile device ID may be referenced to a specific useror group of users of the mobile device (e.g., this reference may bestored in user device information 422).

In another example, data and statistics are carried along with a tokenas it moves from one person to another. The data and statistics may bestored as part of token transaction history 206.

In another example, a user of a mobile device may scan a coupon from awebsite. The coupon is associated with a transactional token managed bytoken processing system 402. The user then applies the token to atransaction at the merchant location associated with the coupon offer(e.g., 30% off at the merchant). The merchant can scan the coupon/tokenin at the point of sale. Token processing system 402 then changes thestate of the token from a coupon into what was described above as atrophy. The trophy data stores when and where the user executed thecoupon.

The trophy connotes a sense of recognition that an event has occurred(e.g., a purchase or other event such as here where the coupon wasredeemed). The trophy stores transaction and other data associated witha redemption. Once the user is in possession of the token that wasapplied, this data stays with the token for as long as the user is inpossession of the token. The token may continue to remain associatedwith either the particular mobile device used in the transaction orother event, or remain with the user (e.g., as may be stored in tokendata 426).

In another example, if a user redeems coupons in order to accumulate apredetermined number of trophies, then the collection of trophies cangenerate another new token/coupon (e.g., by the user exchanging thetrophies for a new token 412). Alternatively, the trophies could beexchanged for some other business or consumer offer, or could enable theuser to access a new section of a website (e.g., a premium section withmore privileges; or an access token for a website).

These trophies also may be used to interface with social websites suchas the Facebook website and/or other websites, or they could be used toimplement an access method if the user has a certain number of tokens.The trophies and tokens are configurable in numerous ways (one coupon ortoken may generate another coupon, or it could turn into anothercoupon).

In one example, an ID for a new token is generated by hashing variouspieces of data together such as the time of day, the user, the device,etc. to create the ID (e.g., a twenty byte hash).

In one example, when a token is used on a particular user device, thetoken is associated with that device. The device itself is part of anetwork, and so the token is then associated with that network. The userof the device or the device itself may be associated (e.g., viastatistics 424 or account information 416) with an IP address and/or toa physical address, so that this data also may be associated with thenetwork.

The foregoing and other various types of data associated with thenetwork (e.g., email addresses and/or zip codes of users in the network)provide data inputs (e.g., transaction data of the user and other usersin the network) to collector 404, which analyzes the data and makeschanges to the state of tokens (either tokens now being generated orexisting tokens) to reflect the business situation and conditions asinferred from the analyzed data (this collector may also point to othertokens such as the user's device token, or to the user's account number,or to the user's transaction history). This process sets up a closeddata feedback loop in which tokens affect transactions, and data fromthe transactions and/or the users associated with the transactionsaffects the state of tokens (new and existing) as reflected by theirvarious attributes (e.g., a real-time change by a merchant in the amountof discount for a purchase that is associated with all tokens in a givennetwork).

In another example of a business loop using tokens, someone other thanthe user may use a token/coupon. For example, the user may be a memberof a social group (e.g., club). When the other member uses the token,the user receives a newly-generated coupon based on usage of the tokenby the other member (or members). The social group may already be partof a previously-defined network and/or a new network may be defined thatincludes the social group and its users.

In another example of a social group, a user is a member of a car club(e.g., there may be thousands of clubs in the United States). Each clubmay be associated with a network or container class, and each club mayhave members that possess tokens as described above. Tokens possessed bymembers of the car club generate data when applied or used, as discussedabove. This data may be used by the token processing system (e.g., viaan artificial intelligence engine) to infer or make correlations to datain other networks (e.g., for a different club). These inferences orcorrelations may be used as a basis for generating new tokens (e.g., forthe different club).

In the example above, new tokens are sent to all members of the car club(e.g., sent to 200 user devices and 50 email addresses of the members).As the new tokens are used, token processing system 402 receives data.In one case, the new token is not actually generated until the email isread by the club member, and the club member clicks on an icon toindicate it wants to acquire the token/coupon. It is at that time that atoken would be generated. Usage of the new tokens leads to generation ofother new tokens based on analysis of the data.

As part of the analysis of this data, the token processing system seesthat the new token is used by 100 people. A new network group is definedthat includes these 100 people. This network group may be used foranalysis by the token processing system. So, in one case, a top-levelnetwork includes all of the devices and all of the email addressesassociated with the car club that use the same token. The intelligenceengine analyzes this network and draws inferences that lead to thegeneration or creation of another type of token/coupon (e.g., itidentifies a relationship between malt shop tokens and old car partstokens).

Data from transactions or other usage of tokens involving or related tothe social group may be correlated with other data in the tokenprocessing system. Various members of the social group may hold varioustypes of tokens and trophies as were described above. The tokenprocessing system analyzes these holdings in deciding what new tokens togenerate, and/or how to change the state of existing tokens (e.g.,tokens in a network that includes the social group). For example, therating of a token may be increased or decreased by this general process(and specific rules may define the specific increases or decreases touse).

In one example, the token processing system uses an artificialintelligence engine that is performing a variety of statistical andmathematic analysis. The engine has both batch and a real-time sides(e.g., the engine may be run on an entire population of users once aday, such as 100 million users). The outcome from the batch processingwould determine the new coupons to be generated for the next day. Thereal-time generation of tokens would be based on transaction data asdescribed above.

In one example, a transactional token points to multiple objects suchas, for example, devices, consumers, and email addresses. The token mayalso further point to one or more networks or network groups. Thus, thetoken may be used as a basis for identifying a correlation between usersin different networks or network groups.

Token Usage in a Local Region or Network

As mentioned above, in some alternative embodiments, rather than stayingwith user 102 after a transaction is completed, a transactional tokenmay transfer from user 102 to merchant 104. In one embodiment, Token₂ istransferred to merchant 104 after the Transaction₁, and merchant 104then holds Token₂. The possession of Token₂ by merchant 104 will beupdated in a memory of the token processing system. The transfer oftokens from one user to another user is used in this embodiment toprovide transactional tokens that are restricted to use, or only havevalue when used, in a local region or network.

Referring again to FIG. 1 , instead of Token₂ being transferred tosupplier 106 as was discussed above, in this embodiment, Token₂ istransferred to a merchant 120. Token processing system 402 may store ina memory or database that user 102, merchant 104, and merchant 120 areall in a defined local region or network. The local region may bedefined geographically such as by a list of street addresses, or zipcodes, or city names, or may be defined by some distance from one ormore geographic locations, or by other various means. Alternatively, anetwork may be defined, for example, as a set of users having a commonnetwork domain, users that are members of a common club or otherorganization (e.g., a social network defined at any of various levels ofa social network or graph), or users that are in compliance with certainstandards, regulations, or other criteria.

In one embodiment, a limited set of transactional tokens may begenerated by token processing system 402. The number of tokens iscontrolled and usage of the tokens is limited to the local region ornetwork. Thus, the tokens may have some of the characteristics of alocal currency.

When the merchant 104 applies Token₂ to the Transaction₃, Token₂ istransferred to merchant 120 by token processing system 402. Token₂ asheld by merchant 120 (i.e., token 122 in this embodiment) has aState_(C) that corresponds to a discount or other benefit for merchant120 when token 122 is applied to a transaction with another entity inthe local region or network (e.g., Transaction₄ with user 102). AfterTransaction₄, Token₂ is transferred to user 102. Thus, the transactionaltoken (and its benefits) remains within the local region or network. Inother embodiments, the tokens may be transferred to entities outside ofthe local region or network, but will not provide a transaction benefituntil applied to a transaction with an entity in the local region ornetwork.

In one embodiment, a method comprises: generating a plurality oftransactional tokens including a first token; associating each of thetransactional tokens with a respective one of a plurality of users, thefirst token associated with a first user of the plurality of users;monitoring usage of the transactional tokens in a plurality oftransactions in a local region or network, the plurality of transactionsincluding a first transaction of the first user; and in response toreceiving transactional data regarding the first transaction, updatingthe first token from a first state to a second state.

In one embodiment, each of the plurality of transactional tokens islimited to transfer to an entity within the local region or network. Inone embodiment, the first token is applied by the first user to thefirst transaction.

In one embodiment, the first transaction is between the first user and asecond user, and the method further comprises responsive to applying thefirst token to the first transaction, transferring the first token tothe second user. The second state corresponds to a transactional benefitfor a future transaction of the second user. The transactional benefitfor the future transaction requires usage of the first token in thelocal region or network.

In one embodiment, the generating the plurality of transactional tokenscomprises generating a limited number of the tokens.

In one embodiment, the first state corresponds to a discount for apurchase from a first merchant, and the second state corresponds to adiscount for a purchase from a second merchant. For example, the firstmerchant and the second merchant are both in the local region ornetwork. In another example, discount is a fixed currency amount or afixed percentage discount.

In one embodiment, the first token comprises a plurality of attributesincluding a first attribute, and in the second state the first attributeprovides a percentage reduction in price for a future transaction of thefirst user. In one example, the method further comprises associating arespective transaction history with each of the transactional tokens,and updating the respective transaction history based on transactions towhich the respective token has been applied (e.g., this history mayinclude all transactions with entities in the local region or network).In one example, the plurality of users includes a second user in thelocal region or network, and the method further comprises responsive toreceiving a request from the first user, transferring the first token tothe second user.

In one embodiment, the tokens are limited to usage with a local network(it should be noted that the usage of the term “local network” does notimply a geographic limitation on the location of the users, but insteadmerely implies a defining of a particular set of users relative to theironline or network presence or usage), and the method further comprisesassociating the transactional tokens with the network.

In one embodiment, the receiving the transactional data is received by atransaction handler configured to receive from acquirer processorsauthorization requests for payments to be made by issuer processorsaccording to account identifiers of users, the issuer processors to makethe payments on behalf of users, and the acquirer processors to receivethe payments on behalf of merchants.

In one embodiment, a company operating a transaction handler (e.g., Visaor American Express) or other entity may issue or generate transactionaltokens that provide discounts for participating merchants within a localregion such as a city or county. These tokens may, for example, beacquired in various ways: accepting a local electronic coupon as amerchant, from a person-to-person transfer via a token processing system(e.g., a gift from one user to another user), or by merchant promotions.The supply of coupons may be limited to the number of transactionswithin a local region. In one example, local regions or communities mayissue company-branded cards (e.g., branded with a brand of thetransaction handler and/or other entity) to promote use of such localcoupons. In one approach, the transactional tokens may be applied orredeemed regardless of the actual payment method (e.g., the tokens orcoupons can be used in conjunction with cash or another alternativepayment method used to make a purchase). This local approach may permitlocal merchants and consumers to reduce the cost of producing goodslocally. Also, in some cases, this local token approach may make it moredifficult for a producer to falsely claim that its product islocally-produced when this is not true.

In other approaches, the usage of local tokens may be used to provideincentives for a desired behavior (e.g., use of a certain greentechnology) and provide the transactional benefits to entities (e.g.,users) that conform with certain standards (e.g., associated with theusage of the green technology). The local region or network may bedefined based on an entity's compliance with these standards (e.g.,entities that comply are added as members of a defined network in tokenprocessing system 402).

The following sections below provide exemplary embodiments (e.g., atransaction handler and a transaction-data-based portal) that may beused in various implementations with the transactional token processingsystems and methods described above, but the following sections do notlimit the generality of these transactional token processing systems andmethods. In some embodiments, the hardware described in the sectiontitled “HARDWARE” below may be used to implement the systems and methodsdescribed above for the token processing infrastructure 400.

Transaction Based Intelligence Information

Millions of transactions occur daily through the use of payment cards,such as credit cards, debit cards, prepaid cards, etc. Correspondingrecords of the transactions are recorded in databases for settlement andfinancial recordkeeping (e.g., to meet the requirements of governmentregulations). Such data can be mined and analyzed for trends,statistics, and other analyses. Sometimes such data are mined forspecific advertising goals, such as to provide targeted offers toaccount holders, as described in PCT Pub. No. WO 2008/067543 A2,published on Jun. 5, 2008 and entitled “Techniques for Targeted Offers.”

U.S. Pat. App. Pub. No. 2009/0216579, published on Aug. 27, 2009 andentitled “Tracking Online Advertising using Payment Services,” disclosesa system in which a payment service identifies the activity of a userusing a payment card as corresponding with an offer associated with anonline advertisement presented to the user.

U.S. Pat. No. 6,298,330, issued on Oct. 2, 2001 and entitled“Communicating with a Computer Based on the Offline Purchase History ofa Particular Consumer,” discloses a system in which a targetedadvertisement is delivered to a computer in response to receiving anidentifier, such as cookie, corresponding to the computer.

U.S. Pat. No. 7,035,855, issued on Apr. 25, 2006 and entitled “Processand System for Integrating Information from Disparate Databases forPurposes of Predicting Consumer Behavior,” discloses a system in whichconsumer transactional information is used for predicting consumerbehavior.

U.S. Pat. No. 6,505,168, issued on Jan. 7, 2003 and entitled “System andMethod for Gathering and Standardizing Customer Purchase Information forTarget Marketing,” discloses a system in which categories andsub-categories are used to organize purchasing information by creditcards, debit cards, checks and the like. The customer purchaseinformation is used to generate customer preference information formaking targeted offers.

U.S. Pat. No. 7,444,658, issued on Oct. 28, 2008 and entitled “Methodand System to Perform Content Targeting,” discloses a system in whichadvertisements are selected to be sent to users based on a userclassification performed using credit card purchasing data.

U.S. Pat. App. Pub. No. 2005/0055275, published on Mar. 10, 2005 andentitled “System and Method for Analyzing Marketing Efforts,” disclosesa system that evaluates the cause and effect of advertising andmarketing programs using card transaction data.

U.S. Pat. App. Pub. No. 2008/0217397, published on Sep. 11, 2008 andentitled “Real-Time Awards Determinations,” discloses a system forfacilitating transactions with real-time awards determinations for acardholder, in which the award may be provided to the cardholder as acredit on the cardholder's statement.

In one embodiment, transaction data, such as records of transactionsmade via credit accounts, debit accounts, prepaid accounts, bankaccounts, stored value accounts and the like, is processed to provideinformation for various services, such as reporting, benchmarking,advertising, content or offer selection, customization, personalization,prioritization, etc. In one embodiment, users are required to enroll ina service program and provide consent to allow the system to use relatedtransaction data and/or other data for the related services. The systemis configured to provide the services while protecting the privacy ofthe users in accordance with the enrollment agreement and user consent.

In one embodiment, an advertising network is provided based on atransaction handler to present personalized or targetedadvertisements/offers on behalf of advertisers. A computing apparatusof, or associated with, the transaction handler uses the transactiondata and/or other data, such as account data, merchant data, searchdata, social networking data, web data, etc., to develop intelligenceinformation about individual customers, or certain types or groups ofcustomers. The intelligence information can be used to select, identify,generate, adjust, prioritize, and/or personalize advertisements/offersto the customers.

In one embodiment, the computing apparatus is to generate triggerrecords for a transaction handler to identify authorization requeststhat satisfy the conditions specified in the trigger records, identifycommunication references of the users associated with the identifiedauthorization requests, and use the communication references to targetreal-time messages at the users in parallel with the transaction handlerproviding responses to the respective authorization requests. Details inone embodiment regarding the generation and delivery of messages inreal-time with the processing of transactions are provided in thesection entitled “REAL-TIME MESSAGES.”

In one embodiment, the computing apparatus correlates transactions withactivities that occurred outside the context of the transaction, such asonline advertisements presented to the customers that at least in partcause the offline transactions. The correlation data can be used todemonstrate the success of the advertisements, and/or to improveintelligence information about how individual customers and/or varioustypes or groups of customers respond to the advertisements.

In one embodiment, the computing apparatus correlates, or providesinformation to facilitate the correlation of, transactions with onlineactivities of the customers, such as searching, web browsing, socialnetworking and consuming advertisements, with other activities, such aswatching television programs, and/or with events, such as meetings,announcements, natural disasters, accidents, news announcements, etc.

In one embodiment, the correlation results are used in predictive modelsto predict transactions and/or spending patterns based on activities orevents, to predict activities or events based on transactions orspending patterns, to provide alerts or reports, etc.

In one embodiment, a single entity operating the transaction handlerperforms various operations in the services provided based on thetransaction data. For example, in the presentation of the personalizedor targeted advertisements, the single entity may perform the operationssuch as generating the intelligence information, selecting relevantintelligence information for a given audience, selecting, identifying,adjusting, prioritizing, personalizing and/or generating advertisementsbased on selected relevant intelligence information, and facilitatingthe delivery of personalized or targeted advertisements, etc.Alternatively, the entity operating the transaction handler cooperateswith one or more other entities by providing information to theseentities to allow these entities to perform at least some of theoperations for presentation of the personalized or targetedadvertisements.

System

FIG. 5 illustrates a system to provide services based on transactiondata according to one embodiment. In FIG. 5 , the system includes atransaction terminal (105) to initiate financial transactions for a user(101), a transaction handler (103) to generate transaction data (109)from processing the financial transactions of the user (101) (and thefinancial transactions of other users), a profile generator (121) togenerate transaction profiles (127) based on the transaction data (109)to provide information/intelligence about user preferences and spendingpatterns, a point of interaction (107) to provide information and/oroffers to the user (101), a user tracker (113) to generate user data(125) to identify the user (101) using the point of interaction (107), aprofile selector (129) to select a profile (131) specific to the user(101) identified by the user data (125), and an advertisement selector(133) to select, identify, generate, adjust, prioritize and/orpersonalize advertisements for presentation to the user (101) on thepoint of interaction (107) via a media controller (115).

In one embodiment, the system further includes a correlator (117) tocorrelate user specific advertisement data (119) with transactionsresulting from the user specific advertisement data (119). Thecorrelation results (123) can be used by the profile generator (121) toimprove the transaction profiles (127).

In one embodiment, the transaction profiles (127) are generated from thetransaction data (109) in a way as illustrated in FIGS. 6 and 7 . Forexample, in FIG. 7 , an aggregated spending profile (341) is generatedvia the factor analysis (327) and cluster analysis (329) to summarize(335) the spending patterns/behaviors reflected in the transactionrecords (301).

In one embodiment, a data warehouse (149) as illustrated in FIG. 8 iscoupled with the transaction handler (103) to store the transaction data(109) and other data, such as account data (111), transaction profiles(127) and correlation results (123). In FIG. 8 , a portal (143) iscoupled with the data warehouse (149) to provide data or informationderived from the transaction data (109), in response to a query requestfrom a third party or as an alert or notification message.

In FIG. 8 , the transaction handler (103) is coupled between an issuerprocessor (145) in control of a consumer account (146) and an acquirerprocessor (147) in control of a merchant account (148). An accountidentification device (141) is configured to carry the accountinformation (142) that identifies the consumer account (146) with theissuer processor (145) and provide the account information (142) to thetransaction terminal (105) of a merchant to initiate a transactionbetween the user (101) and the merchant.

FIGS. 9 and 10 illustrate examples of transaction terminals (105) andaccount identification devices (141). FIG. 11 illustrates the structureof a data processing system that can be used to implement, with more orfewer elements, at least some of the components in the system, such asthe point of interaction (107), the transaction handler (103), theportal (143), the data warehouse, the account identification device(141), the transaction terminal (105), the user tracker (113), theprofile generator (121), the profile selector (129), the advertisementselector (133), the media controller (115), etc. Some embodiments usemore or fewer components than those illustrated in FIGS. 5 and 8-11 , asfurther discussed in the section entitled “VARIATIONS.”

In one embodiment, the transaction data (109) relates to financialtransactions processed by the transaction handler (103); and the accountdata (111) relates to information about the account holders involved inthe transactions. Further data, such as merchant data that relates tothe location, business, products and/or services of the merchants thatreceive payments from account holders for their purchases, can be usedin the generation of the transaction profiles (127, 341).

In one embodiment, the financial transactions are made via an accountidentification device (141), such as financial transaction cards (e.g.,credit cards, debit cards, banking cards, etc.); the financialtransaction cards may be embodied in various devices, such as plasticcards, chips, radio frequency identification (RFID) devices, mobilephones, personal digital assistants (PDAs), etc.; and the financialtransaction cards may be represented by account identifiers (e.g.,account numbers or aliases). In one embodiment, the financialtransactions are made via directly using the account information (142),without physically presenting the account identification device (141).

Further features, modifications and details are provided in varioussections of this description.

Centralized Data Warehouse

In one embodiment, the transaction handler (103) maintains a centralizeddata warehouse (149) organized around the transaction data (109). Forexample, the centralized data warehouse (149) may include, and/orsupport the determination of, spend band distribution, transaction countand amount, merchant categories, merchant by state, cardholdersegmentation by velocity scores, and spending within merchant target,competitive set and cross-section.

In one embodiment, the centralized data warehouse (149) providescentralized management but allows decentralized execution. For example,a third party strategic marketing analyst, statistician, marketer,promoter, business leader, etc., may access the centralized datawarehouse (149) to analyze customer and shopper data, to providefollow-up analyses of customer contributions, to develop propensitymodels for increased conversion of marketing campaigns, to developsegmentation models for marketing, etc. The centralized data warehouse(149) can be used to manage advertisement campaigns and analyze responseprofitability.

In one embodiment, the centralized data warehouse (149) includesmerchant data (e.g., data about sellers), customer/business data (e.g.,data about buyers), and transaction records (301) between sellers andbuyers over time. The centralized data warehouse (149) can be used tosupport corporate sales forecasting, fraud analysis reporting,sales/customer relationship management (CRM) business intelligence,credit risk prediction and analysis, advanced authorization reporting,merchant benchmarking, business intelligence for small business,rewards, etc.

In one embodiment, the transaction data (109) is combined with externaldata, such as surveys, benchmarks, search engine statistics,demographics, competition information, emails, etc., to flag key eventsand data values, to set customer, merchant, data or event triggers, andto drive new transactions and new customer contacts.

Transaction Profile

In FIG. 5 , the profile generator (121) generates transaction profiles(127) based on the transaction data (109), the account data (111),and/or other data, such as non-transactional data, wish lists, merchantprovided information, address information, information from socialnetwork websites, information from credit bureaus, information fromsearch engines, and other examples discussed in U.S. patent applicationSer. No. 12/614,603, filed Nov. 9, 2009 and entitled “Analyzing LocalNon-Transactional Data with Transactional Data in Predictive Models,”the disclosure of which is hereby incorporated herein by reference.

In one embodiment, the transaction profiles (127) provide intelligenceinformation on the behavior, pattern, preference, propensity, tendency,frequency, trend, and budget of the user (101) in making purchases. Inone embodiment, the transaction profiles (127) include information aboutwhat the user (101) owns, such as points, miles, or other rewardscurrency, available credit, and received offers, such as coupons loadedinto the accounts of the user (101). In one embodiment, the transactionprofiles (127) include information based on past offer/coupon redemptionpatterns. In one embodiment, the transaction profiles (127) includeinformation on shopping patterns in retail stores as well as online,including frequency of shopping, amount spent in each shopping trip,distance of merchant location (retail) from the address of the accountholder(s), etc.

In one embodiment, the transaction handler (103) provides at least partof the intelligence for the prioritization, generation, selection,customization and/or adjustment of the advertisement for delivery withina transaction process involving the transaction handler (103). Forexample, the advertisement may be presented to a customer in response tothe customer making a payment via the transaction handler (103).

Some of the transaction profiles (127) are specific to the user (101),or to an account of the user (101), or to a group of users of which theuser (101) is a member, such as a household, family, company,neighborhood, city, or group identified by certain characteristicsrelated to online activities, offline purchase activities, merchantpropensity, etc.

In one embodiment, the profile generator (121) generates and updates thetransaction profiles (127) in batch mode periodically. In otherembodiments, the profile generator (121) generates the transactionprofiles (127) in real-time, or just in time, in response to a requestreceived in the portal (143) for such profiles.

In one embodiment, the transaction profiles (127) include the values fora set of parameters. Computing the values of the parameters may involvecounting transactions that meet one or more criteria, and/or building astatistically-based model in which one or more calculated values ortransformed values are put into a statistical algorithm that weightseach value to optimize its collective predictiveness for variouspredetermined purposes.

Further details and examples about the transaction profiles (127) in oneembodiment are provided in the section entitled “AGGREGATED SPENDINGPROFILE.”

Non-Transactional Data

In one embodiment, the transaction data (109) is analyzed in connectionwith non-transactional data to generate transaction profiles (127)and/or to make predictive models.

In one embodiment, transactions are correlated with non-transactionalevents, such as news, conferences, shows, announcements, market changes,natural disasters, etc. to establish cause and effect relations topredict future transactions or spending patterns. For example,non-transactional data may include the geographic location of a newsevent, the date of an event from an events calendar, the name of aperformer for an upcoming concert, etc. The non-transactional data canbe obtained from various sources, such as newspapers, websites, blogs,social networking sites, etc.

In one embodiment, when the cause and effect relationships between thetransactions and non-transactional events are known (e.g., based onprior research results, domain knowledge, expertise), the relationshipscan be used in predictive models to predict future transactions orspending patterns, based on events that occurred recently or arehappening in real-time.

In one embodiment, the non-transactional data relates to events thathappened in a geographical area local to the user (101) that performedthe respective transactions. In one embodiment, a geographical area islocal to the user (101) when the distance from the user (101) tolocations in the geographical area is within a convenient range fordaily or regular travel, such as 20, 50 or 100 miles from an address ofthe user (101), or within the same city or zip code area of an addressof the user (101). Examples of analyses of local non-transactional datain connection with transaction data (109) in one embodiment are providedin U.S. patent application Ser. No. 12/614,603, filed Nov. 9, 2009 andentitled “Analyzing Local Non-Transactional Data with Transactional Datain Predictive Models,” the disclosure of which is hereby incorporatedherein by reference.

In one embodiment, the non-transactional data is not limited to localnon-transactional data. For example, national non-transactional data canalso be used.

In one embodiment, the transaction records (301) are analyzed infrequency domain to identify periodic features in spending events. Theperiodic features in the past transaction records (301) can be used topredict the probability of a time window in which a similar transactionwould occur. For example, the analysis of the transaction data (109) canbe used to predict when a next transaction having the periodic featurewould occur, with which merchant, the probability of a repeatedtransaction with a certain amount, the probability of exception, theopportunity to provide an advertisement or offer such as a coupon, etc.In one embodiment, the periodic features are detected through countingthe number of occurrences of pairs of transactions that occurred withina set of predetermined time intervals and separating the transactionpairs based on the time intervals. Some examples and techniques for theprediction of future transactions based on the detection of periodicfeatures in one embodiment are provided in U.S. patent application Ser.No. 12/773,770, filed May 4, 2010 and entitled “Frequency-BasedTransaction Prediction and Processing,” the disclosure of which ishereby incorporated herein by reference.

Techniques and details of predictive modeling in one embodiment areprovided in U.S. Pat. Nos. 6,119,103, 6,018,723, 6,658,393, 6,598,030,and 7,227,950, the disclosures of which are hereby incorporated hereinby reference.

In one embodiment, offers are based on the point-of-service to offereedistance to allow the user (101) to obtain in-person services. In oneembodiment, the offers are selected based on transaction history andshopping patterns in the transaction data (109) and/or the distancebetween the user (101) and the merchant. In one embodiment, offers areprovided in response to a request from the user (101), or in response toa detection of the location of the user (101). Examples and details ofat least one embodiment are provided in U.S. patent application Ser. No.11/767,218, filed Jun. 22, 2007, assigned Pub. No. 2008/0319843, andentitled “Supply of Requested Offer Based on Point-of Service to OffereeDistance,” U.S. patent application Ser. No. 11/755,575, filed May 30,2007, assigned Pub. No. 2008/0300973, and entitled “Supply of RequestedOffer Based on Offeree Transaction History,” U.S. patent applicationSer. No. 11/855,042, filed Sep. 13, 2007, assigned Pub. No.2009/0076896, and entitled “Merchant Supplied Offer to a Consumer withina Predetermined Distance,” U.S. patent application Ser. No. 11/855,069,filed Sep. 13, 2007, assigned Pub. No. 2009/0076925, and entitled“Offeree Requested Offer Based on Point-of Service to Offeree Distance,”and U.S. patent application Ser. No. 12/428,312, filed Apr. 22, 2009 andentitled “Receiving an Announcement Triggered by Location Data,” thedisclosures of which applications are hereby incorporated herein byreference.

Targeting Advertisement

In FIG. 5 , an advertisement selector (133) prioritizes, generates,selects, adjusts, and/or customizes the available advertisement data(135) to provide user specific advertisement data (119) based at leastin part on the user specific profile (131). The advertisement selector(133) uses the user specific profile (131) as a filter and/or a set ofcriteria to generate, identify, select and/or prioritize advertisementdata for the user (101). A media controller (115) delivers the userspecific advertisement data (119) to the point of interaction (107) forpresentation to the user (101) as the targeted and/or personalizedadvertisement.

In one embodiment, the user data (125) includes the characterization ofthe context at the point of interaction (107). Thus, the use of the userspecific profile (131), selected using the user data (125), includes theconsideration of the context at the point of interaction (107) inselecting the user specific advertisement data (119).

In one embodiment, in selecting the user specific advertisement data(119), the advertisement selector (133) uses not only the user specificprofile (131), but also information regarding the context at the pointof interaction (107). For example, in one embodiment, the user data(125) includes information regarding the context at the point ofinteraction (107); and the advertisement selector (133) explicitly usesthe context information in the generation or selection of the userspecific advertisement data (119).

In one embodiment, the advertisement selector (133) may query forspecific information regarding the user (101) before providing the userspecific advertisement data (119). The queries may be communicated tothe operator of the transaction handler (103) and, in particular, to thetransaction handler (103) or the profile generator (121). For example,the queries from the advertisement selector (133) may be transmitted andreceived in accordance with an application programming interface orother query interface of the transaction handler (103), the profilegenerator (121) or the portal (143) of the transaction handler (103).

In one embodiment, the queries communicated from the advertisementselector (133) may request intelligence information regarding the user(101) at any level of specificity (e.g., segment level, individuallevel). For example, the queries may include a request for a certainfield or type of information in a cardholder's aggregate spendingprofile (341). As another example, the queries may include a request forthe spending level of the user (101) in a certain merchant category overa prior time period (e.g., six months).

In one embodiment, the advertisement selector (133) is operated by anentity that is separate from the entity that operates the transactionhandler (103). For example, the advertisement selector (133) may beoperated by a search engine, a publisher, an advertiser, an ad network,or an online merchant. The user specific profile (131) is provided tothe advertisement selector (133) to assist the customization of the userspecific advertisement data (119).

In one embodiment, advertising is targeted based on shopping patterns ina merchant category (e.g., as represented by a Merchant Category Code(MCC)) that has high correlation of spending propensity with othermerchant categories (e.g., other MCCs). For example, in the context of afirst MCC for a targeted audience, a profile identifying second MCCsthat have high correlation of spending propensity with the first MCC canbe used to select advertisements for the targeted audience.

In one embodiment, the aggregated spending profile (341) is used toprovide intelligence information about the spending patterns,preferences, and/or trends of the user (101). For example, a predictivemodel can be established based on the aggregated spending profile (341)to estimate the needs of the user (101). For example, the factor values(344) and/or the cluster ID (343) in the aggregated spending profile(341) can be used to determine the spending preferences of the user(101). For example, the channel distribution (345) in the aggregatedspending profile (341) can be used to provide a customized offertargeted for a particular channel, based on the spending patterns of theuser (101).

In one embodiment, mobile advertisements, such as offers and coupons,are generated and disseminated based on aspects of prior purchases, suchas timing, location, and nature of the purchases, etc. In oneembodiment, the size of the benefit of the offer or coupon is based onpurchase volume or spending amount of the prior purchase and/or thesubsequent purchase that may qualify for the redemption of the offer.Further details and examples of one embodiment are provided in Prov.U.S. patent application Ser. No. 11/960,162, filed Dec. 19, 2007,assigned Pub. No. 2008/0201226, and entitled “Mobile Coupon Method andPortable Consumer Device for Utilizing Same,” the disclosure of which ishereby incorporated herein by reference.

In one embodiment, conditional rewards are provided to the user (101);and the transaction handler (103) monitors the transactions of the user(101) to identify redeemable rewards that have satisfied the respectiveconditions. In one embodiment, the conditional rewards are selectedbased on transaction data (109). Further details and examples of oneembodiment are provided in U.S. patent application Ser. No. 11/862,487,filed Sep. 27, 2007 and entitled “Consumer Specific ConditionalRewards,” the disclosure of which is hereby incorporated herein byreference. The techniques to detect the satisfied conditions ofconditional rewards can also be used to detect the transactions thatsatisfy the conditions specified to locate the transactions that resultfrom online activities, such as online advertisements, searches, etc.,to correlate the transactions with the respective online activities.

Further details about targeted offer delivery in one embodiment areprovided in U.S. patent application Ser. No. 12/185,332, filed Aug. 4,2008, assigned Pub. No. 2010/0031644, and entitled “Targeted Advertisingby Payment Processor History of Cashless Acquired Merchant Transactionon Issued Consumer Account,” and in U.S. patent application Ser. No.12/849,793, filed Aug. 3, 2010 and entitled “Systems and Methods forTargeted Advertisement Delivery,” the disclosures of which are herebyincorporated herein by reference.

Profile Matching

In FIG. 5 , the user tracker (113) obtains and generates contextinformation about the user (101) at the point of interaction (107),including user data (125) that characterizes and/or identifies the user(101). The profile selector (129) selects a user specific profile (131)from the set of transaction profiles (127) generated by the profilegenerator (121), based on matching the characteristics of thetransaction profiles (127) and the characteristics of the user data(125). For example, the user data (125) indicates a set ofcharacteristics of the user (101); and the profile selector (129)selects the user specific profile (131) that is for a particular user ora group of users and that best matches the set of characteristicsspecified by the user data (125).

In one embodiment, the profile selector (129) receives the transactionprofiles (127) in a batch mode. The profile selector (129) selects theuser specific profile (131) from the batch of transaction profiles (127)based on the user data (125). Alternatively, the profile generator (121)generates the transaction profiles (127) in real-time; and the profileselector (129) uses the user data (125) to query the profile generator(121) to generate the user specific profile (131) in real-time, or justin time. The profile generator (121) generates the user specific profile(131) that best matches the user data (125).

In one embodiment, the user tracker (113) identifies the user (101)based on the user activity on the transaction terminal (105) (e.g.,having visited a set of websites, currently visiting a type of webpages, search behavior, etc.).

In one embodiment, the user data (125) includes an identifier of theuser (101), such as a global unique identifier (GUID), a personalaccount number (PAN) (e.g., credit card number, debit card number, orother card account number), or other identifiers that uniquely andpersistently identify the user (101) within a set of identifiers of thesame type. Alternatively, the user data (125) may include otheridentifiers, such as an Internet Protocol (IP) address of the user(101), a name or user name of the user (101), or a browser cookie ID,which identify the user (101) in a local, temporary, transient and/oranonymous manner. Some of these identifiers of the user (101) may beprovided by publishers, advertisers, ad networks, search engines,merchants, or the user tracker (113). In one embodiment, suchidentifiers are correlated to the user (101) based on the overlapping orproximity of the time period of their usage to establish anidentification reference table.

In one embodiment, the identification reference table is used toidentify the account information (142) (e.g., account number (312))based on characteristics of the user (101) captured in the user data(125), such as browser cookie ID, IP addresses, and/or timestamps on theusage of the IP addresses. In one embodiment, the identificationreference table is maintained by the operator of the transaction handler(103). Alternatively, the identification reference table is maintainedby an entity other than the operator of the transaction handler (103).

In one embodiment, the user tracker (113) determines certaincharacteristics of the user (101) to describe a type or group of usersof which the user (101) is a member. The transaction profile of thegroup is used as the user specific profile (131). Examples of suchcharacteristics include geographical location or neighborhood, types ofonline activities, specific online activities, or merchant propensity.In one embodiment, the groups are defined based on aggregate information(e.g., by time of day, or household), or segment (e.g., by cluster,propensity, demographics, cluster IDs, and/or factor values). In oneembodiment, the groups are defined in part via one or more socialnetworks. For example, a group may be defined based on social distancesto one or more users on a social network website, interactions betweenusers on a social network website, and/or common data in social networkprofiles of the users in the social network website.

In one embodiment, the user data (125) may match different profiles at adifferent granularity or resolution (e.g., account, user, family,company, neighborhood, etc.), with different degrees of certainty. Theprofile selector (129) and/or the profile generator (121) may determineor select the user specific profile (131) with the finest granularity orresolution with acceptable certainty. Thus, the user specific profile(131) is most specific or closely related to the user (101).

In one embodiment, the advertisement selector (133) uses further data inprioritizing, selecting, generating, customizing and adjusting the userspecific advertisement data (119). For example, the advertisementselector (133) may use search data in combination with the user specificprofile (131) to provide benefits or offers to a user (101) at the pointof interaction (107). For example, the user specific profile (131) canbe used to personalize the advertisement, such as adjusting theplacement of the advertisement relative to other advertisements,adjusting the appearance of the advertisement, etc.

Browser Cookie

In one embodiment, the user data (125) uses browser cookie informationto identify the user (101). The browser cookie information is matched toaccount information (142) or the account number (312) to identify theuser specific profile (131), such as aggregated spending profile (341)to present effective, timely, and relevant marketing information to theuser (101), via the preferred communication channel (e.g., mobilecommunications, web, mail, email, POS, etc.) within a window of timethat could influence the spending behavior of the user (101). Based onthe transaction data (109), the user specific profile (131) can improveaudience targeting for online advertising. Thus, customers will getbetter advertisements and offers presented to them; and the advertiserswill achieve better return-on-investment for their advertisementcampaigns.

In one embodiment, the browser cookie that identifies the user (101) inonline activities, such as web browsing, online searching, and usingsocial networking applications, can be matched to an identifier of theuser (101) in account data (111), such as the account number (312) of afinancial payment card of the user (101) or the account information(142) of the account identification device (141) of the user (101). Inone embodiment, the identifier of the user (101) can be uniquelyidentified via matching IP address, timestamp, cookie ID and/or otheruser data (125) observed by the user tracker (113).

In one embodiment, a look up table is used to map browser cookieinformation (e.g., IP address, timestamp, cookie ID) to the account data(111) that identifies the user (101) in the transaction handler (103).The look up table may be established via correlating overlapping orcommon portions of the user data (125) observed by different entities ordifferent user trackers (113).

For example, in one embodiment, a first user tracker (113) observes thecard number of the user (101) at a particular IP address for a timeperiod identified by a timestamp (e.g., via an online payment process);a second user tracker (113) observes the user (101) having a cookie IDat the same IP address for a time period near or overlapping with thetime period observed by the first user tracker (113). Thus, the cookieID as observed by the second user tracker (113) can be linked to thecard number of the user (101) as observed by the first user tracker(113). The first user tracker (113) may be operated by the same entityoperating the transaction handler (103) or by a different entity. Oncethe correlation between the cookie ID and the card number is establishedvia a database or a look up table, the cookie ID can be subsequentlyused to identify the card number of the user (101) and the account data(111).

In one embodiment, the portal (143) is configured to observe a cardnumber of a user (101) while the user (101) uses an IP address to makean online transaction. Thus, the portal (143) can identify a consumeraccount (146) based on correlating an IP address used to identify theuser (101) and IP addresses recorded in association with the consumeraccount (146).

For example, in one embodiment, when the user (101) makes a paymentonline by submitting the account information (142) to the transactionterminal (105) (e.g., an online store), the transaction handler (103)obtains the IP address from the transaction terminal (105) via theacquirer processor (147). The transaction handler (103) stores data toindicate the use of the account information (142) at the IP address atthe time of the transaction request. When an IP address in the queryreceived in the portal (143) matches the IP address previously recordedby the transaction handler (103), the portal (143) determines that theuser (101) identified by the IP address in the request is the same user(101) associated with the account of the transaction initiated at the IPaddress. In one embodiment, a match is found when the time of the queryrequest is within a predetermined time period from the transactionrequest, such as a few minutes, one hour, a day, etc. In one embodiment,the query may also include a cookie ID representing the user (101).Thus, through matching the IP address, the cookie ID is associated withthe account information (142) in a persistent way.

In one embodiment, the portal (143) obtains the IP address of the onlinetransaction directly. For example, in one embodiment, a user (101)chooses to use a password in the account data (111) to protect theaccount information (142) for online transactions. When the accountinformation (142) is entered into the transaction terminal (105) (e.g.,an online store or an online shopping cart system), the user (101) isconnected to the portal (143) for the verification of the password(e.g., via a pop up window, or via redirecting the web browser of theuser (101)). The transaction handler (103) accepts the transactionrequest after the password is verified via the portal (143). Throughthis verification process, the portal (143) and/or the transactionhandler (103) obtain the IP address of the user (101) at the time theaccount information (142) is used.

In one embodiment, the web browser of the user (101) communicates theuser provided password to the portal (143) directly without goingthrough the transaction terminal (105) (e.g., the server of themerchant). Alternatively, the transaction terminal (105) and/or theacquirer processor (147) may relay the password communication to theportal (143) or the transaction handler (103).

In one embodiment, the portal (143) is configured to identify theconsumer account (146) based on the IP address identified in the userdata (125) through mapping the IP address to a street address. Forexample, in one embodiment, the user data (125) includes an IP addressto identify the user (101); and the portal (143) can use a service tomap the IP address to a street address. For example, an Internet serviceprovider knows the street address of the currently assigned IP address.Once the street address is identified, the portal (143) can use theaccount data (111) to identify the consumer account (146) that has acurrent address at the identified street address. Once the consumeraccount (146) is identified, the portal (143) can provide a transactionprofile (131) specific to the consumer account (146) of the user (101).

In one embodiment, the portal (143) uses a plurality of methods toidentify consumer accounts (146) based on the user data (125). Theportal (143) combines the results from the different methods todetermine the most likely consumer account (146) for the user data(125).

Details about the identification of consumer account (146) based on userdata (125) in one embodiment are provided in U.S. patent applicationSer. No. 12/849,798, filed Aug. 3, 2010 and entitled “Systems andMethods to Match Identifiers,” the disclosure of which is herebyincorporated herein by reference.

Close the Loop

In one embodiment, the correlator (117) is used to “close the loop” forthe tracking of consumer behavior across an on-line activity and an“off-line” activity that results at least in part from the on-lineactivity. In one embodiment, online activities, such as searching, webbrowsing, social networking, and/or consuming online advertisements, arecorrelated with respective transactions to generate the correlationresult (123) in FIG. 5 . The respective transactions may occur offline,in “brick and mortar” retail stores, or online but in a context outsidethe online activities, such as a credit card purchase that is performedin a way not visible to a search company that facilitates the searchactivities.

In one embodiment, the correlator (117) is to identify transactionsresulting from searches or online advertisements. For example, inresponse to a query about the user (101) from the user tracker (113),the correlator (117) identifies an offline transaction performed by theuser (101) and sends the correlation result (123) about the offlinetransaction to the user tracker (113), which allows the user tracker(113) to combine the information about the offline transaction and theonline activities to provide significant marketing advantages.

For example, a marketing department could correlate an advertisingbudget to actual sales. For example, a marketer can use the correlationresult (123) to study the effect of certain prioritization strategies,customization schemes, etc. on the impact on the actual sales. Forexample, the correlation result (123) can be used to adjust orprioritize advertisement placement on a web site, a search engine, asocial networking site, an online marketplace, or the like.

In one embodiment, the profile generator (121) uses the correlationresult (123) to augment the transaction profiles (127) with dataindicating the rate of conversion from searches or advertisements topurchase transactions. In one embodiment, the correlation result (123)is used to generate predictive models to determine what a user (101) islikely to purchase when the user (101) is searching using certainkeywords or when the user (101) is presented with an advertisement oroffer. In one embodiment, the portal (143) is configured to report thecorrelation result (123) to a partner, such as a search engine, apublisher, or a merchant, to allow the partner to use the correlationresult (123) to measure the effectiveness of advertisements and/orsearch result customization, to arrange rewards, etc.

Illustratively, a search engine entity may display a search page withparticular advertisements for flat panel televisions produced bycompanies A, B, and C. The search engine entity may then compare theparticular advertisements presented to a particular consumer withtransaction data of that consumer and may determine that the consumerpurchased a flat panel television produced by Company B. The searchengine entity may then use this information and other informationderived from the behavior of other consumers to determine theeffectiveness of the advertisements provided by companies A, B, and C.The search engine entity can determine if the placement, the appearance,or other characteristic of the advertisement results in actual increasedsales. Adjustments to advertisements (e.g., placement, appearance, etc.)may be made to facilitate maximum sales.

In one embodiment, the correlator (117) matches the online activitiesand the transactions based on matching the user data (125) provided bythe user tracker (113) and the records of the transactions, such astransaction data (109) or transaction records (301). In anotherembodiment, the correlator (117) matches the online activities and thetransactions based on the redemption of offers/benefits provided in theuser specific advertisement data (119).

In one embodiment, the portal (143) is configured to receive a set ofconditions and an identification of the user (101), determine whetherthere is any transaction of the user (101) that satisfies the set ofconditions, and if so, provide indications of the transactions thatsatisfy the conditions and/or certain details about the transactions,which allows the requester to correlate the transactions with certainuser activities, such as searching, web browsing, consumingadvertisements, etc.

In one embodiment, the requester may not know the account number (312)of the user (101); and the portal (143) is to map the identifierprovided in the request to the account number (312) of the user (101) toprovide the requested information. Examples of the identifier beingprovided in the request to identify the user (101) include anidentification of an iFrame of a web page visited by the user (101), abrowser cookie ID, an IP address and the day and time corresponding tothe use of the IP address, etc.

The information provided by the portal (143) can be used in pre-purchasemarketing activities, such as customizing content or offers,prioritizing content or offers, selecting content or offers, etc., basedon the spending pattern of the user (101). The content that iscustomized, prioritized, selected, or recommended may be the searchresults, blog entries, items for sale, etc.

The information provided by the portal (143) can be used inpost-purchase activities. For example, the information can be used tocorrelate an offline purchase with online activities. For example, theinformation can be used to determine purchases made in response to mediaevents, such as television programs, advertisements, news announcements,etc.

Details about profile delivery, online activity to offline purchasetracking, techniques to identify the user specific profile (131) basedon user data (125) (such as IP addresses), and targeted delivery ofadvertisement/offer/benefit in some embodiments are provided in U.S.patent application Ser. No. 12/849,789, filed Aug. 3, 2010 and entitled“Systems and Methods for Closing the Loop between Online Activities andOffline Purchases,” the disclosure of which application is incorporatedherein by reference.

Matching Advertisement & Transaction

In one embodiment, the correlator (117) is configured to receiveinformation about the user specific advertisement data (119), monitorthe transaction data (109), identify transactions that can be consideredresults of the advertisement corresponding to the user specificadvertisement data (119), and generate the correlation result (123), asillustrated in FIG. 5 .

When the advertisement and the corresponding transaction both occur inan online checkout process, a website used for the online checkoutprocess can be used to correlate the transaction and the advertisement.However, the advertisement and the transaction may occur in separateprocesses and/or under control of different entities (e.g., when thepurchase is made offline at a retail store, while the advertisement ispresented outside the retail store). In one embodiment, the correlator(117) uses a set of correlation criteria to identify the transactionsthat can be considered as the results of the advertisements.

In one embodiment, the correlator (117) identifies the transactionslinked or correlated to the user specific advertisement data (119) basedon various criteria. For example, the user specific advertisement data(119) may include a coupon offering a benefit contingent upon a purchasemade according to the user specific advertisement data (119). The use ofthe coupon identifies the user specific advertisement data (119), andthus allows the correlator (117) to correlate the transaction with theuser specific advertisement data (119).

In one embodiment, the user specific advertisement data (119) isassociated with the identity or characteristics of the user (101), suchas global unique identifier (GUID), personal account number (PAN),alias, IP address, name or user name, geographical location orneighborhood, household, user group, and/or user data (125). Thecorrelator (117) can link or match the transactions with theadvertisements based on the identity or characteristics of the user(101) associated with the user specific advertisement data (119). Forexample, the portal (143) may receive a query identifying the user data(125) that tracks the user (101) and/or characteristics of the userspecific advertisement data (119); and the correlator (117) identifiesone or more transactions matching the user data (125) and/or thecharacteristics of the user specific advertisement data (119) togenerate the correlation result (123).

In one embodiment, the correlator (117) identifies the characteristicsof the transactions and uses the characteristics to search foradvertisements that match the transactions. Such characteristics mayinclude GUID, PAN, IP address, card number, browser cookie information,coupon, alias, etc.

In FIG. 5 , the profile generator (121) uses the correlation result(123) to enhance the transaction profiles (127) generated from theprofile generator (121). The correlation result (123) provides detailson the purchases and/or indicates the effectiveness of the user specificadvertisement data (119).

In one embodiment, the correlation result (123) is used to demonstrateto the advertisers the effectiveness of the advertisements, to processincentive or rewards associated with the advertisements, to obtain atleast a portion of advertisement revenue based on the effectiveness ofthe advertisements, to improve the selection of advertisements, etc.

Coupon Matching

In one embodiment, the correlator (117) identifies a transaction that isa result of an advertisement (e.g., 119) when an offer or benefitprovided in the advertisement is redeemed via the transaction handler(103) in connection with a purchase identified in the advertisement.

For example, in one embodiment, when the offer is extended to the user(101), information about the offer can be stored in association with theaccount of the user (101) (e.g., as part of the account data (111)). Theuser (101) may visit the portal (143) of the transaction handler (103)to view the stored offer.

The offer stored in the account of the user (101) may be redeemed viathe transaction handler (103) in various ways. For example, in oneembodiment, the correlator (117) may download the offer to thetransaction terminal (105) via the transaction handler (103) when thecharacteristics of the transaction at the transaction terminal (105)match the characteristics of the offer.

After the offer is downloaded to the transaction terminal (105), thetransaction terminal (105) automatically applies the offer when thecondition of the offer is satisfied in one embodiment. Alternatively,the transaction terminal (105) allows the user (101) to selectivelyapply the offers downloaded by the correlator (117) or the transactionhandler (103). In one embodiment, the correlator (117) sends remindersto the user (101) at a separate point of interaction (107) (e.g., amobile phone) to remind the user (101) to redeem the offer. In oneembodiment, the transaction handler (103) applies the offer (e.g., viastatement credit), without having to download the offer (e.g., coupon)to the transaction terminal (105). Examples and details of redeemingoffers via statement credit are provided in U.S. patent application Ser.No. 12/566,350, filed Sep. 24, 2009 and entitled “Real-Time StatementCredits and Notifications,” the disclosure of which is herebyincorporated herein by reference.

In one embodiment, the offer is captured as an image and stored inassociation with the account of the user (101). Alternatively, the offeris captured in a text format (e.g., a code and a set of criteria),without replicating the original image of the coupon.

In one embodiment, when the coupon is redeemed, the advertisementpresenting the coupon is correlated with a transaction in which thecoupon is redeemed, and/or is determined to have resulted in atransaction. In one embodiment, the correlator (117) identifiesadvertisements that have resulted in purchases, without having toidentify the specific transactions that correspond to theadvertisements.

Details about offer redemption via the transaction handler (103) in oneembodiment are provided in U.S. patent application Ser. No. 12/849,801,filed Aug. 3, 2010 and entitled “Systems and Methods for Multi-ChannelOffer Redemption,” the disclosure of which is hereby incorporated hereinby reference.

On ATM & POS Terminal

In one example, the transaction terminal (105) is an automatic tellermachine (ATM), which is also the point of interaction (107). When theuser (101) approaches the ATM to make a transaction (e.g., to withdrawcash via a credit card or debit card), the ATM transmits accountinformation (142) to the transaction handler (103). The accountinformation (142) can also be considered as the user data (125) toselect the user specific profile (131). The user specific profile (131)can be sent to an advertisement network to query for a targetedadvertisement. After the advertisement network matches the user specificprofile (131) with user specific advertisement data (119) (e.g., atargeted advertisement), the transaction handler (103) may send theadvertisement to the ATM, together with the authorization for cashwithdrawal.

In one embodiment, the advertisement shown on the ATM includes a couponthat offers a benefit that is contingent upon the user (101) making apurchase according to the advertisement. The user (101) may view theoffer presented on a white space on the ATM screen and select to load orstore the coupon in a storage device of the transaction handler (103)under the account of the user (101). The transaction handler (103)communicates with the bank to process the cash withdrawal. After thecash withdrawal, the ATM prints the receipt which includes aconfirmation of the coupon, or a copy of the coupon. The user (101) maythen use the coupon printed on the receipt. Alternatively, when the user(101) uses the same account to make a relevant purchase, the transactionhandler (103) may automatically apply the coupon stored under theaccount of the user (101), or automatically download the coupon to therelevant transaction terminal (105), or transmit the coupon to themobile phone of the user (101) to allow the user (101) to use the couponvia a display of the coupon on the mobile phone. The user (101) mayvisit a web portal (143) of the transaction handler (103) to view thestatus of the coupons collected in the account of the user (101).

In one embodiment, the advertisement is forwarded to the ATM via thedata stream for authorization. In another embodiment, the ATM makes aseparate request to a server of the transaction handler (103) (e.g., aweb portal) to obtain the advertisement. Alternatively, or incombination, the advertisement (including the coupon) is provided to theuser (101) at separate, different points of interactions, such as via atext message to a mobile phone of the user (101), via an email, via abank statement, etc.

Details of presenting targeted advertisements on ATMs based onpurchasing preferences and location data in one embodiment are providedin U.S. patent application Ser. No. 12/266,352, filed Nov. 6, 2008 andentitled “System Including Automated Teller Machine with Data BearingMedium,” the disclosure of which is hereby incorporated herein byreference.

In another example, the transaction terminal (105) is a point of sale(POS) terminal at the checkout station in a retail store (e.g., aself-service checkout register). When the user (101) pays for a purchasevia a payment card (e.g., a credit card or a debit card), thetransaction handler (103) provides a targeted advertisement having acoupon obtained from an advertisement network. The user (101) may loadthe coupon into the account of the payment card and/or obtain a hardcopyof the coupon from the receipt. When the coupon is used in atransaction, the advertisement is linked to the transaction.

Details of presenting targeted advertisements during the process ofauthorizing a financial payment card transaction in one embodiment areprovided in U.S. patent application Ser. No. 11/799,549, filed May 1,2007, assigned Pub. No. 2008/0275771, and entitled “Merchant TransactionBased Advertising,” the disclosure of which is hereby incorporatedherein by reference.

In one embodiment, the user specific advertisement data (119), such asoffers or coupons, is provided to the user (101) via the transactionterminal (105) in connection with an authorization message during theauthorization of a transaction processed by the transaction handler(103). The authorization message can be used to communicate the rewardsqualified for by the user (101) in response to the current transaction,the status and/or balance of rewards in a loyalty program, etc. Examplesand details related to the authorization process in one embodiment areprovided in U.S. patent application Ser. No. 11/266,766, filed Nov. 2,2005, assigned Pub. No. 2007/0100691, and entitled “Method and Systemfor Conducting Promotional Programs,” the disclosure of which is herebyincorporated herein by reference.

In one embodiment, when the user (101) is conducting a transaction witha first merchant via the transaction handler (103), the transactionhandler (103) may determine whether the characteristics of thetransaction satisfy the conditions specified for an announcement, suchas an advertisement, offer or coupon, from a second merchant. If theconditions are satisfied, the transaction handler (103) provides theannouncement to the user (101). In one embodiment, the transactionhandler (103) may auction the opportunity to provide the announcementsto a set of merchants. Examples and details related to the delivery ofsuch announcements in one embodiment are provided in U.S. patentapplication Ser. No. 12/428,241, filed Apr. 22, 2009 and entitled“Targeting Merchant Announcements Triggered by Consumer ActivityRelative to a Surrogate Merchant,” the disclosure of which is herebyincorporated herein by reference.

On Third Party Site

In a further example, the user (101) may visit a third party website,which is the point of interaction (107) in FIG. 5 . The third partywebsite may be a web search engine, a news website, a blog, a socialnetwork site, etc. The behavior of the user (101) at the third partywebsite may be tracked via a browser cookie, which uses a storage spaceof the browser to store information about the user (101) at the thirdparty website. Alternatively, or in combination, the third party websiteuses the server logs to track the activities of the user (101). In oneembodiment, the third party website may allow an advertisement networkto present advertisements on portions of the web pages. Theadvertisement network tracks the user behavior using its server logsand/or browser cookies. For example, the advertisement network may use abrowser cookie to identify a particular user across multiple websites.Based on the referral uniform resource locators (URL) that cause theadvertisement network to load advertisements in various web pages, theadvertisement network can determine the online behavior of the user(101) via analyzing the web pages that the user (101) has visited. Basedon the tracked online activities of the user (101), the user data (125)that characterizes the user (101) can be formed to query the profilerselector (129) for a user specific profile (131).

In one embodiment, the cookie identity of the user (101) as trackedusing the cookie can be correlated to an account of the user (101), thefamily of the user (101), the company of the user (101), or other groupsthat include the user (101) as a member. Thus, the cookie identity canbe used as the user data (125) to obtain the user specific profile(131). For example, when the user (101) makes an online purchase from aweb page that contains an advertisement that is tracked with the cookieidentity, the cookie identity can be correlated to the onlinetransaction and thus to the account of the user (101). For example, whenthe user (101) visits a web page after authentication of the user (101),and the web page includes an advertisement from the advertisementnetwork, the cookie identity can be correlated to the authenticatedidentity of the user (101). For example, when the user (101) signs in toa web portal of the transaction handler (103) to access the account ofthe user (101), the cookie identity used by the advertisement network onthe web portal can be correlated to the account of the user (101).

Other online tracking techniques can also be used to correlate thecookie identity of the user (101) with an identifier of the user (101)known by the profile selector (129), such as a GUID, PAN, accountnumber, customer number, social security number, etc. Subsequently, thecookie identity can be used to select the user specific profile (131).

Multiple Communications

In one embodiment, the entity operating the transaction handler (103)may provide intelligence for providing multiple communications regardingan advertisement. The multiple communications may be directed to two ormore points of interaction with the user (101).

For example, after the user (101) is provided with an advertisement viathe transaction terminal (105), reminders or revisions to theadvertisements can be sent to the user (101) via a separate point ofinteraction (107), such as a mobile phone, email, text message, etc. Forexample, the advertisement may include a coupon to offer the user (101)a benefit contingent upon a purchase. If the correlator (117) determinesthat the coupon has not been redeemed, the correlator (117) may send amessage to the mobile phone of the user (101) to remind the user (101)about the offer, and/or revise the offer.

Examples of multiple communications related to an offer in oneembodiment are provided in U.S. patent application Ser. No. 12/510,167,filed Jul. 27, 2009 and entitled “Successive Offer Communications withan Offer Recipient,” the disclosure of which is hereby incorporatedherein by reference.

Auction Engine

In one embodiment, the transaction handler (103) provides a portal toallow various clients to place bids according to clusters (e.g., totarget entities in the clusters for marketing, monitoring, researching,etc.)

For example, the cardholders may register in a program to receiveoffers, such as promotions, discounts, sweepstakes, reward points,direct mail coupons, email coupons, etc. The cardholders may registerwith issuers, or with the portal (143) of the transaction handler (103).Based on the transaction data (109) or transaction records (301) and/orthe registration data, the profile generator (121) is to identify theclusters of cardholders and the values representing the affinity of thecardholders to the clusters. Various entities may place bids accordingto the clusters and/or the values to gain access to the cardholders,such as the user (101). For example, an issuer may bid on access tooffers; an acquirer and/or a merchant may bid on customer segments. Anauction engine receives the bids and awards segments and offers based onthe received bids. Thus, the customers can get great deals; andmerchants can get customer traffic and thus sales.

Some techniques to identify a segment of users (101) for marketing areprovided in U.S. patent application Ser. No. 12/288,490, filed Oct. 20,2008, assigned Pub. No. 2009/0222323, and entitled “OpportunitySegmentation,” U.S. patent application Ser. No. 12/108,342, filed Apr.23, 2008, assigned Pub. No. 2009/0271305, and entitled “PaymentPortfolio Optimization,” and U.S. patent application Ser. No.12/108,354, filed Apr. 23, 2008, assigned Pub. No. 2009/0271327, andentitled “Payment Portfolio Optimization,” the disclosures of whichapplications are hereby incorporated herein by reference.

Social Network Validation

In one embodiment, the transaction data (109) is combined with socialnetwork data and/or search engine data to provide benefits (e.g.,coupons) to a consumer. For example, a data exchange apparatus mayidentify cluster data based upon consumer search engine data, socialnetwork data, and payment transaction data to identify like groups ofindividuals who would respond favorably to particular types of benefitssuch as coupons and statement credits. Advertisement campaigns may beformulated to target the cluster of cardholders.

In one embodiment, search engine data is combined with social networkdata and/or the transaction data (109) to evaluate the effectiveness ofthe advertisements and/or conversion pattern of the advertisements. Forexample, after a search engine displays advertisements about flat paneltelevisions to a consumer, a social network that is used by a consumermay provide information about a related purchase made by the consumer.For example, the blog of the consumer, and/or the transaction data(109), may indicate that the flat panel television purchased by theconsumer is from company B. Thus, the search engine data and the socialnetwork data and/or the transaction data (109) can be combined tocorrelate advertisements to purchases resulting from the advertisementsand to determine the conversion pattern of the advertisement to theconsumer. Adjustments to advertisements (e.g., placement, appearance,etc.) can be made to improve the effectiveness of the advertisements andthus increase sales.

Loyalty Program

In one embodiment, the transaction handler (103) uses the account data(111) to store information for third party loyalty programs. Thetransaction handler (103) processes payment transactions made viafinancial transaction cards, such as credit cards, debit cards, bankingcards, etc.; and the financial transaction cards can be used as loyaltycards for the respective third party loyalty programs. Since the thirdparty loyalty programs are hosted on the transaction handler (103), theconsumers do not have to carry multiple, separate loyalty cards (e.g.,one for each merchant that offers a loyalty program); and the merchantsdo not have to spend a large setup and investment fee to establish theloyalty program. The loyalty programs hosted on the transaction handler(103) can provide flexible awards for consumers, retailers,manufacturers, issuers, and other types of business entities involved inthe loyalty programs. The integration of the loyalty programs into theaccounts of the customers on the transaction handler (103) allows newofferings, such as merchant cross-offerings or bundling of loyaltyofferings.

In one embodiment, an entity operating the transaction handler (103)hosts loyalty programs for third parties using the account data (111) ofthe users (e.g., 101). A third party, such as a merchant, a retailer, amanufacturer, an issuer or other entity that is interested in promotingcertain activities and/or behaviors, may offer loyalty rewards onexisting accounts of consumers. The incentives delivered by the loyaltyprograms can drive behavior changes without the hassle of loyalty cardcreation. In one embodiment, the loyalty programs hosted via theaccounts of the users (e.g., 101) of the transaction handler (103) allowthe consumers to carry fewer cards and may provide more data to themerchants than traditional loyalty programs.

The loyalty programs integrated with the accounts of the users (e.g.,101) of the transaction handler (103) can provide tools to enable nimbleprograms that are better aligned for driving changes in consumerbehaviors across transaction channels (e.g., online, offline, via mobiledevices). The loyalty programs can be ongoing programs that accumulatebenefits for the customers (e.g., points, miles, cash back), and/orprograms that provide one time benefits or limited time benefits (e.g.,rewards, discounts, incentives).

FIG. 12 shows the structure of account data (111) for providing loyaltyprograms according to one embodiment. In FIG. 12 , data related to athird party loyalty program may include an identifier of the loyaltybenefit offeror (183) that is linked to a set of loyalty program rules(185) and loyalty record (187) for the loyalty program activities of theaccount identifier (181). In one embodiment, at least part of the datarelated to the third party loyalty program is stored under the accountidentifier (181) of the user (101), such as the loyalty record (187).

FIG. 12 illustrates the data related to one third party loyalty programof a loyalty benefit offeror (183). In one embodiment, the accountidentifier (181) may be linked to multiple loyalty benefit offerors(e.g., 183), corresponding to different third party loyalty programs.

In one embodiment, a third party loyalty program of the loyalty benefitofferor (183) provides the user (101), identified by the accountidentifier (181), with benefits, such as discounts, rewards, incentives,cash back, gifts, coupons, and/or privileges.

In one embodiment, the association between the account identifier (181)and the loyalty benefit offeror (183) in the account data (111)indicates that the user (101) having the account identifier (181) is amember of the loyalty program. Thus, the user (101) may use the accountidentifier (181) to access privileges afforded to the members of theloyalty programs, such as rights to access a member only area, facility,store, product or service, discounts extended only to members, oropportunities to participate in certain events, buy certain items, orreceive certain services reserved for members.

In one embodiment, it is not necessary to make a purchase to use theprivileges. The user (101) may enjoy the privileges based on the statusof being a member of the loyalty program. The user (101) may use theaccount identifier (181) to show the status of being a member of theloyalty program.

For example, the user (101) may provide the account identifier (181)(e.g., the account number of a credit card) to the transaction terminal(105) to initiate an authorization process for a special transactionwhich is designed to check the member status of the user (101), as ifthe account identifier (181) were used to initiate an authorizationprocess for a payment transaction. The special transaction is designedto verify the member status of the user (101) via checking whether theaccount data (111) is associated with the loyalty benefit offeror (183).If the account identifier (181) is associated with the correspondingloyalty benefit offeror (183), the transaction handler (103) provides anapproval indication in the authorization process to indicate that theuser (101) is a member of the loyalty program. The approval indicationcan be used as a form of identification to allow the user (101) toaccess member privileges, such as access to services, products,opportunities, facilities, discounts, permissions, which are reservedfor members.

In one embodiment, when the account identifier (181) is used to identifythe user (101) as a member to access member privileges, the transactionhandler (103) stores information about the access of the correspondingmember privilege in loyalty record (187). The profile generator (121)may use the information accumulated in the loyalty record (187) toenhance transaction profiles (127) and provide the user (101) withpersonalized/targeted advertisements, with or without further offers ofbenefit (e.g., discounts, incentives, rebates, cash back, rewards,etc.).

In one embodiment, the association of the account identifier (181) andthe loyalty benefit offeror (183) also allows the loyalty benefitofferor (183) to access at least a portion of the account data (111)relevant to the loyalty program, such as the loyalty record (187) andcertain information about the user (101), such as name, address, andother demographic data.

In one embodiment, the loyalty program allows the user (101) toaccumulate benefits according to loyalty program rules (185), such asreward points, cash back, levels of discounts, etc. For example, theuser (101) may accumulate reward points for transactions that satisfythe loyalty program rules (185); and the user (101) may use the rewardpoints to redeem cash, gift, discounts, etc. In one embodiment, theloyalty record (187) stores the accumulated benefits; and thetransaction handler (103) updates the loyalty record (187) associatedwith the loyalty benefit offeror (183) and the account identifier (181),when events that satisfy the loyalty program rules occur.

In one embodiment, the accumulated benefits as indicated in the loyaltyrecord (187) can be redeemed when the account identifier (181) is usedto perform a payment transaction, when the payment transaction satisfiesthe loyalty program rules. For example, the user (101) may redeem anumber of points to offset or reduce an amount of the purchase price.

In one embodiment, when the user (101) uses the account identifier (181)to make purchases as a member, the merchant may further provideinformation about the purchases; and the transaction handler (103) canstore the information about the purchases as part of the loyalty record(187). The information about the purchases may identify specific itemsor services purchased by the member. For example, the merchant mayprovide the transaction handler (103) with purchase details atstock-keeping unit (SKU) level, which are then stored as part of theloyalty record (187). The loyalty benefit offeror (183) may use thepurchase details to study the purchase behavior of the user (101); andthe profile generator (121) may use the SKU level purchase details toenhance the transaction profiles (127).

In one embodiment, the SKU level purchase details are requested from themerchants or retailers via authorization responses, when the account(146) of the user (101) is enrolled in a loyalty program that allows thetransaction handler (103) (and/or the issuer processor (145)) to collectthe purchase details.

In one embodiment, the profile generator (121) may generate transactionprofiles (127) based on the loyalty record (187) and provide thetransaction profiles (127) to the loyalty benefit offeror (183) (orother entities when permitted).

In one embodiment, the loyalty benefit offeror (183) may use thetransaction profiles (e.g., 127 or 131) to select candidates formembership offering. For example, the loyalty program rules (185) mayinclude one or more criteria that can be used to identify whichcustomers are eligible for the loyalty program. The transaction handler(103) may be configured to automatically provide the qualified customerswith the offer of membership in the loyalty program when thecorresponding customers are performing transactions via the transactionhandler (103) and/or via points of interaction (107) accessible to theentity operating the transaction handler (103), such as ATMs, mobilephones, receipts, statements, websites, etc. The user (101) may acceptthe membership offer via responding to the advertisement. For example,the user (101) may load the membership into the account in the same wayas loading a coupon into the account of the user (101).

In one embodiment, the membership offer is provided as a coupon or isassociated with another offer of benefits, such as a discount, reward,etc. When the coupon or benefit is redeemed via the transaction handler(103), the account data (111) is updated to enroll the user (101) intothe corresponding loyalty program.

In one embodiment, a merchant may enroll a user (101) into a loyaltyprogram when the user (101) is making a purchase at the transactionterminal (105) of the merchant.

For example, when the user (101) is making a transaction at an ATM,performing a self-assisted check out on a POS terminal, or making apurchase transaction on a mobile phone or a computer, the user (101) maybe prompted to join a loyalty program, while the transaction is beingauthorized by the transaction handler (103). If the user (101) acceptsthe membership offer, the account data (111) is updated to have theaccount identifier (181) associated with the loyalty benefit offeror(183).

In one embodiment, the user (101) may be automatically enrolled in theloyalty program, when the profile of the user (101) satisfies a set ofconditions specified in the loyalty program rules (185). The user (101)may opt out of the loyalty program.

In one embodiment, the loyalty benefit offeror (183) may personalizeand/or target loyalty benefits based on the transaction profile (131)specific to or linked to the user (101). For example, the loyaltyprogram rules (185) may use the user specific profile (131) to selectgifts, rewards, or incentives for the user (101) (e.g., to redeembenefits, such as reward points, accumulated in the loyalty record(187)). The user specific profile (131) may be enhanced using theloyalty record (187), or generated based on the loyalty record (187).For example, the profile generator (121) may use a subset of transactiondata (109) associated with the loyalty record (187) to generate the userspecific profile (131), or provide more weight to the subset of thetransaction data (109) associated with the loyalty record (187) whilealso using other portions of the transaction data (109) in deriving theuser specific profile (131).

In one embodiment, the loyalty program may involve different entities.For example, a first merchant may offer rewards as discounts, or giftsfrom a second merchant that has a business relationship with the firstmerchant. For example, an entity may allow a user (101) to accumulateloyalty benefits (e.g., reward points) via purchase transactions at agroup of different merchants. For example, a group of merchants mayjointly offer a loyalty program, in which loyalty benefits (e.g., rewardpoints) can be accumulated from purchases at any of the merchants in thegroup and redeemable in purchases at any of the merchants.

In one embodiment, the information identifying the user (101) as amember of a loyalty program is stored on a server connected to thetransaction handler (103). Alternatively or in combination, theinformation identifying the user (101) as a member of a loyalty programcan also be stored in the financial transaction card (e.g., in the chip,or in the magnetic strip).

In one embodiment, loyalty program offerors (e.g., merchants,manufactures, issuers, retailers, clubs, organizations, etc.) cancompete with each other in making loyalty program related offers. Forexample, loyalty program offerors may place bids on loyalty programrelated offers; and the advertisement selector (133) (e.g., under thecontrol of the entity operating the transaction handler (103), or adifferent entity) may prioritize the offers based on the bids. When theoffers are accepted or redeemed by the user (101), the loyalty programofferors pay fees according to the corresponding bids. In oneembodiment, the loyalty program offerors may place an auto bid ormaximum bid, which specifies the upper limit of a bid; and the actualbid is determined to be the lowest possible bid that is larger than thebids of the competitors, without exceeding the upper limit.

In one embodiment, the offers are provided to the user (101) in responseto the user (101) being identified by the user data (125). If the userspecific profile (131) satisfies the conditions specified in the loyaltyprogram rules (185), the offer from the loyalty benefit offeror (183)can be presented to the user (101). When there are multiple offers fromdifferent offerors, the offers can be prioritized according to the bids.

In one embodiment, the offerors can place bids based on thecharacteristics that can be used as the user data (125) to select theuser specific profile (131). In another embodiment, the bids can beplaced on a set of transaction profiles (127).

In one embodiment, the loyalty program based offers are provided to theuser (101) just in time when the user (101) can accept and redeem theoffers. For example, when the user (101) is making a payment for apurchase from a merchant, an offer to enroll in a loyalty programoffered by the merchant or related offerors can be presented to the user(101). If the user (101) accepts the offer, the user (101) is entitledto receive member discounts for the purchase.

For example, when the user (101) is making a payment for a purchase froma merchant, a reward offer can be provided to the user (101) based onloyalty program rules (185) and the loyalty record (187) associated withthe account identifier (181) of the user (101)(e.g., the reward pointsaccumulated in a loyalty program). Thus, the user effort for redeemingthe reward points can be reduced; and the user experience can beimproved.

In one embodiment, a method to provide loyalty programs includes the useof a computing apparatus of a transaction handler (103). The computingapparatus processes (301) a plurality of payment card transactions.After the computing apparatus receives (303) a request to tracktransactions for a loyalty program, such as the loyalty program rules(185), the computing apparatus stores and updates (305) loyalty programinformation in response to transactions occurring in the loyaltyprogram. The computing apparatus provides (307) to a customer (e.g.,101) an offer of a benefit when the customer satisfies a conditiondefined in the loyalty program, such as the loyalty program rules (185).

Examples of loyalty programs through collaboration between collaborativeconstituents in a payment processing system, including the transactionhandler (103) in one embodiment are provided in U.S. patent applicationSer. No. 11/767,202, filed Jun. 22, 2007, assigned Pub. No.2008/0059312, and entitled “Loyalty Program Service,” U.S. patentapplication Ser. No. 11/848,112, filed Aug. 30, 2007, assigned Pub. No.2008/0059316, and entitled “Loyalty Program Incentive Determination,”and U.S. patent application Ser. No. 11/848,179, filed Aug. 30, 2007,assigned Pub. No. 2008/0059307, and entitled “Loyalty Program ParameterCollaboration,” the disclosures of which applications are herebyincorporated herein by reference.

Examples of processing the redemption of accumulated loyalty benefitsvia the transaction handler (103) in one embodiment are provided in U.S.patent application Ser. No. 11/835,100, filed Aug. 7, 2007, assignedPub. No. 2008/0059303, and entitled “Transaction Evaluation forProviding Rewards,” the disclosure of which is hereby incorporatedherein by reference.

In one embodiment, the incentive, reward, or benefit provided in theloyalty program is based on the presence of correlated relatedtransactions. For example, in one embodiment, an incentive is providedif a financial payment card is used in a reservation system to make areservation and the financial payment card is subsequently used to payfor the reserved good or service. Further details and examples of oneembodiment are provided in U.S. patent application Ser. No. 11/945,907,filed Nov. 27, 2007, assigned Pub. No. 2008/0071587, and entitled“Incentive Wireless Communication Reservation,” the disclosure of whichis hereby incorporated herein by reference.

In one embodiment, the transaction handler (103) provides centralizedloyalty program management, reporting and membership services. In oneembodiment, membership data is downloaded from the transaction handler(103) to acceptance point devices, such as the transaction terminal(105). In one embodiment, loyalty transactions are reported from theacceptance point devices to the transaction handler (103); and the dataindicating the loyalty points, rewards, benefits, etc. are stored on theaccount identification device (141). Further details and examples of oneembodiment are provided in U.S. patent application Ser. No. 10/401,504,filed Mar. 27, 2003, assigned Pub. No. 2004/0054581, and entitled“Network Centric Loyalty System,” the disclosure of which is herebyincorporated herein by reference.

In one embodiment, the portal (143) of the transaction handler (103) isused to manage reward or loyalty programs for entities such as issuers,merchants, etc. The cardholders, such as the user (101), are rewardedwith offers/benefits from merchants. The portal (143) and/or thetransaction handler (103) track the transaction records for themerchants for the reward or loyalty programs. Further details andexamples of one embodiment are provided in U.S. patent application Ser.No. 11/688,423, filed Mar. 20, 2007, assigned Pub. No. 2008/0195473, andentitled “Reward Program Manager,” the disclosure of which is herebyincorporated herein by reference.

In one embodiment, a loyalty program includes multiple entitiesproviding access to detailed transaction data, which allows theflexibility for the customization of the loyalty program. For example,issuers or merchants may sponsor the loyalty program to provide rewards;and the portal (143) and/or the transaction handler (103) stores theloyalty currency in the data warehouse (149). Further details andexamples of one embodiment are provided in U.S. patent application Ser.No. 12/177,530, filed Jul. 22, 2008, assigned Pub. No. 2009/0030793, andentitled “Multi-Vender Multi-Loyalty Currency Program,” the disclosureof which is hereby incorporated herein by reference.

In one embodiment, an incentive program is created on the portal (143)of the transaction handler (103). The portal (143) collects offers froma plurality of merchants and stores the offers in the data warehouse(149). The offers may have associated criteria for their distributions.The portal (143) and/or the transaction handler (103) may recommendoffers based on the transaction data (109). In one embodiment, thetransaction handler (103) automatically applies the benefits of theoffers during the processing of the transactions when the transactionssatisfy the conditions associated with the offers. In one embodiment,the transaction handler (103) communicates with transaction terminals(105) to set up, customize, and/or update offers based on market focus,product categories, service categories, targeted consumer demographics,etc. Further details and examples of one embodiment are provided in U.S.patent application Ser. No. 12/413,097, filed Mar. 27, 2009, assignedPub. No. 2010-0049620, and entitled “Merchant Device Support of anIntegrated Offer Network,” the disclosure of which is herebyincorporated herein by reference.

In one embodiment, the transaction handler (103) is configured toprovide offers from merchants to the user (101) via the payment system,making accessing and redeeming the offers convenient for the user (101).The offers may be triggered by and/or tailored to a previoustransaction, and may be valid only for a limited period of time startingfrom the date of the previous transaction. If the transaction handler(103) determines that a subsequent transaction processed by thetransaction handler (103) meets the conditions for the redemption of anoffer, the transaction handler (103) may credit the consumer account(146) for the redemption of the offer and/or provide a notificationmessage to the user (101). Further details and examples of oneembodiment are provided in U.S. patent application Ser. No. 12/566,350,filed Sep. 24, 2009 and entitled “Real-Time Statement Credits andNotifications,” the disclosure of which is hereby incorporated herein byreference.

Details on loyalty programs in one embodiment are provided in U.S.patent application Ser. No. 12/896,632, filed Oct. 1, 2010 and entitled“Systems and Methods to Provide Loyalty Programs,” the disclosure ofwhich is hereby incorporated herein by reference.

SKU

In one embodiment, merchants generate stock-keeping unit (SKU) or otherspecific information that identifies the particular goods and servicespurchased by the user (101) or customer. The SKU information may beprovided to the operator of the transaction handler (103) that processedthe purchases. The operator of the transaction handler (103) may storethe SKU information as part of transaction data (109), and reflect theSKU information for a particular transaction in a transaction profile(127 or 131) associated with the person involved in the transaction.

When a user (101) shops at a traditional retail store or browses awebsite of an online merchant, an SKU-level profile associatedspecifically with the user (101) may be provided to select anadvertisement appropriately targeted to the user (101) (e.g., via mobilephones, POS terminals, web browsers, etc.). The SKU-level profile forthe user (101) may include an identification of the goods and serviceshistorically purchased by the user (101). In addition, the SKU-levelprofile for the user (101) may identify goods and services that the user(101) may purchase in the future. The identification may be based onhistorical purchases reflected in SKU-level profiles of otherindividuals or groups that are determined to be similar to the user(101). Accordingly, the return on investment for advertisers andmerchants can be greatly improved.

In one embodiment, the user specific profile (131) is an aggregatedspending profile (341) that is generated using the SKU-levelinformation. For example, in one embodiment, the factor values (344)correspond to factor definitions (331) that are generated based onaggregating spending in different categories of products and/orservices. A typical merchant offers products and/or services in manydifferent categories.

In one embodiment, the user (101) may enter into transactions withvarious online and “brick and mortar” merchants. The transactions mayinvolve the purchase of various items of goods and services. The goodsand services may be identified by SKU numbers or other information thatspecifically identifies the goods and services purchased by the user(101).

In one embodiment, the merchant may provide the SKU informationregarding the goods and services purchased by the user (101) (e.g.,purchase details at SKU level) to the operator of the transactionhandler (103). In one embodiment, the SKU information may be provided tothe operator of the transaction handler (103) in connection with aloyalty program, as described in more detail below. The SKU informationmay be stored as part of the transaction data (109) and associated withthe user (101). In one embodiment, the SKU information for itemspurchased in transactions facilitated by the operator of the transactionhandler (103) may be stored as transaction data (109) and associatedwith its associated purchaser. In one embodiment, the SKU level purchasedetails are requested from the merchants or retailers via authorizationresponses, when the account (146) of the user (101) is enrolled in aprogram that allows the transaction handler (103) (and/or the issuerprocessor (145)) to collect the purchase details.

In one embodiment, based on the SKU information and perhaps othertransaction data, the profile generator (121) may create an SKU-leveltransaction profile for the user (101). In one embodiment, based on theSKU information associated with the transactions for each personentering into transactions with the operator of the transaction handler(103), the profile generator (121) may create an SKU-level transactionprofile for each person.

In one embodiment, the SKU information associated with a group ofpurchasers may be aggregated to create an SKU-level transaction profilethat is descriptive of the group. The group may be defined based on oneor a variety of considerations. For example, the group may be defined bycommon demographic features of its members. As another example, thegroup may be defined by common purchasing patterns of its members.

In one embodiment, the user (101) may later consider the purchase ofadditional goods and services. The user (101) may shop at a traditionalretailer or an online retailer. With respect to an online retailer, forexample, the user (101) may browse the website of an online retailer,publisher, or merchant. The user (101) may be associated with a browsercookie to, for example, identify the user (101) and track the browsingbehavior of the user (101).

In one embodiment, the retailer may provide the browser cookieassociated with the user (101) to the operator of the transactionhandler (103). Based on the browser cookie, the operator of thetransaction handler (103) may associate the browser cookie with apersonal account number of the user (101). The association may beperformed by the operator of the transaction handler (103) or anotherentity in a variety of manners such as, for example, using a look uptable.

Based on the personal account number, the profile selector (129) mayselect a user specific profile (131) that constitutes the SKU-levelprofile associated specifically with the user (101). The SKU-levelprofile may reflect the individual, prior purchases of the user (101)specifically, and/or the types of goods and services that the user (101)has purchased.

The SKU-level profile for the user (101) may also includeidentifications of goods and services the user (101) may purchase in thefuture. In one embodiment, the identifications may be used for theselection of advertisements for goods and services that may be ofinterest to the user (101). In one embodiment, the identifications forthe user (101) may be based on the SKU-level information associated withhistorical purchases of the user (101). In one embodiment, theidentifications for the user (101) may be additionally or alternativelybased on transaction profiles associated with others. Therecommendations may be determined by predictive association and otheranalytical techniques.

For example, the identifications for the user (101) may be based on thetransaction profile of another person. The profile selector (129) mayapply predetermined criteria to identify another person who, to apredetermined degree, is deemed sufficiently similar to the user (101).The identification of the other person may be based on a variety offactors including, for example, demographic similarity and/or purchasingpattern similarity between the user (101) and the other person. As oneexample, the common purchase of identical items or related items by theuser (101) and the other person may result in an association between theuser (101) and the other person, and a resulting determination that theuser (101) and the other person are similar. Once the other person isidentified, the transaction profile constituting the SKU-level profilefor the other person may be analyzed. Through predictive association andother modeling and analytical techniques, the historical purchasesreflected in the SKU-level profile for the other person may be employedto predict the future purchases of the user (101).

As another example, the identifications of the user (101) may be basedon the transaction profiles of a group of persons. The profile selector(129) may apply predetermined criteria to identify a multitude ofpersons who, to a predetermined degree, are deemed sufficiently similarto the user (101). The identification of the other persons may be basedon a variety of factors including, for example, demographic similarityand/or purchasing pattern similarity between the user (101) and theother persons. Once the group constituting the other persons isidentified, the transaction profile constituting the SKU-level profilefor the group may be analyzed. Through predictive association and othermodeling and analytical techniques, the historical purchases reflectedin the SKU-level profile for the group may be employed to predict thefuture purchases of the user (101).

The SKU-level profile of the user (101) may be provided to select anadvertisement that is appropriately targeted. Because the SKU-levelprofile of the user (101) may include identifications of the goods andservices that the user (101) may be likely to buy, advertisementscorresponding to the identified goods and services may be presented tothe user (101). In this way, targeted advertising for the user (101) maybe optimized. Further, advertisers and publishers of advertisements mayimprove their return on investment, and may improve their ability tocross-sell goods and services.

In one embodiment, SKU-level profiles of others who are identified to besimilar to the user (101) may be used to identify a user (101) who mayexhibit a high propensity to purchase goods and services. For example,if the SKU-level profiles of others reflect a quantity or frequency ofpurchase that is determined to satisfy a threshold, then the user (101)may also be classified or predicted to exhibit a high propensity topurchase. Accordingly, the type and frequency of advertisements thataccount for such propensity may be appropriately tailored for the user(101).

In one embodiment, the SKU-level profile of the user (101) may reflecttransactions with a particular merchant or merchants. The SKU-levelprofile of the user (101) may be provided to a business that isconsidered a peer with or similar to the particular merchant ormerchants. For example, a merchant may be considered a peer of thebusiness because the merchant offers goods and services that are similarto or related to those of the business. The SKU-level profile reflectingtransactions with peer merchants may be used by the business to betterpredict the purchasing behavior of the user (101) and to optimize thepresentation of targeted advertisements to the user (101).

Details on SKU-level profile in one embodiment are provided in U.S.patent application Ser. No. 12/899,144, filed Oct. 6, 2010 and entitled“Systems and Methods for Advertising Services Based on an SKU-LevelProfile,” the disclosure of which is hereby incorporated herein byreference.

Real-Time Messages

In one embodiment, the transaction handler (103) is configured tocooperate with the media controller (115) to facilitate real-timeinteraction with the user (101) when the payment of the user (101) isbeing processed by the transaction handler (103). The real-timeinteraction provides the opportunity to impact the user experienceduring the purchase (e.g., at the time of card swipe), throughdelivering messages in real-time to a point of interaction (107), suchas a mobile phone, a personal digital assistant, a portable computer,etc. The real-time message can be delivered via short message service(SMS), email, instant messaging, or other communications protocols.

In one embodiment, the real-time message is provided without requiringmodifications to existing systems used by the merchants and/or issuers.

FIG. 13 shows a system to provide real-time messages according to oneembodiment. In FIG. 13 , the transaction handler (103) (or a separatecomputing system coupled with the transaction handler (103)) is todetect the occurrence of certain transactions of interest during theprocessing of the authorization requests received from the transactionterminal (105); a message broker (201) is to identify a relevant messagefor the user (101) associated with the corresponding authorizationrequest; and the media controller (115) is to provide the message to theuser (101) at the point of interaction (107) via a communication channelseparate from the channel used by the transaction handler (103) torespond to the corresponding authorization request submitted from thetransaction terminal (105).

In one embodiment, the media controller (115) is to provide the messageto the point of interaction (107) in parallel with the transactionhandler (103) providing the response to the authorization request.

In one embodiment, the point of interaction (107) receives the messagefrom the media controller (115) in real-time with the transactionhandler (103) processing the authorization request. In one embodiment,the message is to arrive at the point of interaction (107) in thecontext of the response provided from the transaction handler (103) tothe transaction terminal (105). For example, the message is to arrive atthe point of interaction (107) substantially at the same time as theresponse to the authorization request arrives at the transactionterminal, or with a delay not long enough to cause the user (101) tohave the impression that the message is in response to an action otherthat the payment transaction. For example, the message is to arrive atthe point of interaction (107) prior to the user (101) completing thetransaction and leaving the transaction terminal (105), or prior to theuser (101) leaving the retail location of the merchant operating thetransaction terminal (105).

In FIG. 13 , the system includes a portal (143) to provide services tomerchants and/or the user (101).

For example, in one embodiment, the portal (143) allows the user (101)to register the communication reference (205) in association with theaccount data (111), such as the account information (142) of theconsumer account (146); and the media controller (115) is to use thecommunication reference (205) to deliver the message to the point ofinteraction (107). Examples of the communication reference (205)includes a mobile phone number, an email address, a user identifier ofan instant messaging system, an IP address, etc.

In one embodiment, the portal (143) allows merchants and/or otherparties to define rules (203) to provide offers (186) as real-timeresponses to authorization requests; and based on the offer rules (203),the message broker (201) is to generate, or instruct the mediacontroller to generate, the real-time message to provide the offers(186) to the user (101). For example, the offer (186) may include adiscount, an incentive, a reward, a rebate, a gift, or other benefit,which can be redeemed upon the satisfaction of certain conditionsrequired by the offer rules (203). In one embodiment, based on the offerrules (203) the message broker (201) configures a message by selectingthe appropriate message template from (an) existing message(s)template(s), and inserts any relevant data (e.g., the communicationreference (205)) into the selected template, then passes the configuredmessage to the media controller (115), which delivers the message to thepoint of interaction (107). In one embodiment, the message broker (201)(or a subsystem) is used to manage message templates along with therules for selecting the appropriate message template from among severalpotential choices.

In one embodiment, the offer rules (203) include offer details,targeting rules, advertisement campaign details, profile mapping,creative mapping, qualification rules, award/notify/fulfillment rules,approvals, etc. Creative elements for offers include text, images,channels, approvals, etc.

In one embodiment, when the offer rules (203) are activated by themerchant or advertiser via the portal (143), the message broker (201) isto generate trigger records (207) for the transaction handler (103). Thetransaction handler (103) is to monitor the incoming authorizationrequests to identify requests that satisfy the conditions specified inthe trigger records (207) during the process of the authorizationrequests, and to provide the information about the identified requeststo the message broker (201) for the transmission of an appropriatereal-time message in accordance with the offer rules (203).

In one embodiment, the generation of the trigger records (207) for thetransaction handler (103) is in real-time with the merchant oradvertiser activating the offer rules (203). Thus, the offer rules (203)can be activated and used for the detection of the new authorizationrequests in real-time, while the transaction handler (103) continues toprocess the incoming authorization requests.

In one embodiment, the portal (143) provides information about thespending behaviors reflected in the transaction data (109) to assist themerchants or advertisers to target offers or advertisements. Forexample, in one embodiment, the portal (143) allows merchants to targetthe offers (186) based on transaction profiles (127). For example, theoffer rules (203) are partially based on the values in a transactionprofile (127), such as an aggregated spending profile (341). In oneembodiment, the offer rules (203) are partially based on the informationabout the last purchase of the user (101) from the merchant operatingthe transaction terminal (105) (or another merchant), and/or theinformation about the location of the user (101), such as the locationdetermined based on the location of the transaction terminal (105)and/or the location of the merchant operating the transaction terminal(105).

In one embodiment, the portal (143) provides transaction basedstatistics, such as merchant benchmarking statistics, industry/marketsegmentation, etc., to assist merchants and advertisers to identifycustomers.

Thus, the real-time messages can be used to influence customer behaviorswhile the customers are in the purchase mode.

In one embodiment, the benefit of the offers (186) can be redeemed viathe transaction handler (103). The redemption of the offer (186) may ormay not require the purchase details (e.g., SKU level purchase details).Details in one embodiment about redeeming offers (186) via thetransaction handler (103) are provided in U.S. patent application Ser.No. 13/113,710, filed May 23, 2011 and entitled “Systems and Methods forRedemption of Offers,” the disclosure of which is hereby incorporatedherein by reference.

In one embodiment, when the authorization request for a purchaseindicates that the purchase qualifies the offer (186) for redemption ifthe purchase corresponding to the authorization request is completed,the message broker (201) is to construct a message and use the mediacontroller (115) to deliver the message in real-time with the processingof the authorization request to the point of interaction (107). Themessage informs the user (101) that when the purchase is completed, thetransaction handler (103) and/or the issuer processor (145) is toprovide the benefit of the offer (186) to the user (101) via statementcredit or some other settlement value, for example points in aregistered loyalty program, or credit at the point of sale using adigital coupon delivered to the purchaser via cell phone.

In one embodiment, the settlement of the payment transactioncorresponding to the authorization request does not occur in real-timewith the processing of the authorization request. For example, themerchant may submit the complete purchases for settlement at the end ofthe day, or in accordance with a predetermined schedule. The settlementmay occur one or more days after the processing of the authorizationrequest.

In one embodiment, when transactions are settled, the settledtransactions are matched to the authorization requests to identifyoffers (186) that are redeemable in view of the settlement. When theoffer (186) is confirmed to be redeemable based on a record ofsuccessful settlement, the message broker (201) is to use the mediacontroller (115) to provide a message to the point of interaction (107)of the user (101), such as the mobile phone of the user (101). In oneembodiment, the message is to inform the user (101) of the benefit to beprovided as statement credits and/or to provide additional offers. Inone embodiment, the message to confirm the statement credits istransmitted in real-time with the completion of the transactionsettlement.

In one embodiment, the message broker (201) is to determine the identityof the merchant based on the information included in the authorizationrequest transmitted from the transaction terminal (105) to thetransaction handler (103). In one embodiment, the identity of themerchant is normalized to allow the application of the offer rules (203)that are merchant specific.

In one embodiment, the portal (143) is to provide data insight tomerchants and/or advertisers. For example, the portal (143) can providethe transaction profile (127) of the user (101), audience segmentationinformation, etc.

In one embodiment, the portal (143) is to allow the merchants and/oradvertisers to define and manage offers for their creation, fulfillmentand/or delivery in messages.

In one embodiment, the portal (143) allows the merchants and/oradvertisers to test, run and/or monitor the offers (186) for theircreation, fulfillment and/or delivery in messages.

In one embodiment, the portal (143) is to provide reports and analyticsregarding the offers (186).

In one embodiment, the portal (143) provides operation facilities, suchas onboarding, contact management, certification, file management,workflow, etc. to assist the merchants and/or advertisers to completethe tasks related to the offers (186).

In one embodiment, the portal (143) allows the user (101) to opt in oropt out of the real-time message delivery service.

In one embodiment, an advertiser or merchant can select an offerfulfillment method from a list of options, such as statement credits,points, gift cards, e-certificates, third party fulfillment, etc.

In one embodiment, the merchant or advertiser is to use the “off therack” transaction profiles (127) available in the data warehouse (149).In one embodiment, the merchant or advertiser can further editparameters to customize the generation of the transaction profiles (127)and/or develop custom transaction profiles from scratch using the portal(143).

In one embodiment, the portal (143) provides a visualization tool toallow the user to see clusters of data based on GeoCodes, proximity,transaction volumes, spending patterns, zip codes, customers, stores,etc.

In one embodiment, the portal (143) allows the merchant or advertiser todefine cells for targeting the customers in the cells based ondate/time, profile attributes, map to offer/channel/creative, conditiontesting, etc.

In one embodiment, the portal (143) allows the merchant or advertiser tomonitor the system health, such as the condition of servers, filesreceived or sent, errors, status, etc., the throughput by date or range,by program, by campaign, or by global view, and aspects of currentprograms/offers/campaigns, such as offer details, package audit reports,etc. In one embodiment, reporting includes analytics and metrics, suchas lift, conversion, category differentials (e.g., spending patterns,transaction volumes, peer groups), and reporting by program, campaign,cell, GeoCode, proximity, ad-hoc, auditing, etc.

FIG. 14 shows a method to provide real-time messages according to oneembodiment. In FIG. 14 , a computing apparatus is to generate (211) atrigger record (207) for a transaction handler (103) to identify anauthorization request that satisfies the conditions specified in thetrigger record (207), receive (213) from the transaction handler (103)information about the authorization request in real-time with thetransaction handler (103) providing a response to the authorizationrequest to a transaction terminal (105), identify (215) a communicationreference (205) of a user (101) associated with the authorizationrequest, determine (217) a message for the user (101) responsive to theauthorization request, and provide (219) the message to the user (101)at a point of interaction (107) via the communication reference (205),in parallel with the response from the transaction handler (103) to thetransaction terminal (105).

In one embodiment, the computing apparatus includes at least one of: atransaction handler, a message broker (201), a media controller (115), aportal (143) and a data warehouse.

Variations

Some embodiments use more or fewer components than those illustrated inFIGS. 5 and 8-11 . For example, in one embodiment, the user specificprofile (131) is used by a search engine to prioritize search results.In one embodiment, the correlator (117) is to correlate transactionswith online activities, such as searching, web browsing, and socialnetworking, instead of or in addition to the user specific advertisementdata (119). In one embodiment, the correlator (117) is to correlatetransactions and/or spending patterns with news announcements, marketchanges, events, natural disasters, etc. In one embodiment, the data tobe correlated by the correlator with the transaction data (109) may notbe personalized via the user specific profile (131) and may not be userspecific. In one embodiment, multiple different devices are used at thepoint of interaction (107) for interaction with the user (101); and someof the devices may not be capable of receiving input from the user(101). In one embodiment, there are transaction terminals (105) toinitiate transactions for a plurality of users (101) with a plurality ofdifferent merchants. In one embodiment, the account information (142) isprovided to the transaction terminal (105) directly (e.g., via phone orInternet) without the use of the account identification device (141).

In one embodiment, at least some of the profile generator (121),correlator (117), profile selector (129), and advertisement selector(133) are controlled by the entity that operates the transaction handler(103). In another embodiment, at least some of the profile generator(121), correlator (117), profile selector (129), and advertisementselector (133) are not controlled by the entity that operates thetransaction handler (103).

For example, in one embodiment, the entity operating the transactionhandler (103) provides the intelligence (e.g., transaction profiles(127) or the user specific profile (131)) for the selection of theadvertisement; and a third party (e.g., a web search engine, apublisher, or a retailer) may present the advertisement in a contextoutside a transaction involving the transaction handler (103) before theadvertisement results in a purchase.

For example, in one embodiment, the customer may interact with the thirdparty at the point of interaction (107); and the entity controlling thetransaction handler (103) may allow the third party to query forintelligence information (e.g., transaction profiles (127), or the userspecific profile (131)) about the customer using the user data (125),thus informing the third party of the intelligence information fortargeting the advertisements, which can be more useful, effective andcompelling to the user (101). For example, the entity operating thetransaction handler (103) may provide the intelligence informationwithout generating, identifying or selecting advertisements; and thethird party receiving the intelligence information may identify, selectand/or present advertisements.

Through the use of the transaction data (109), account data (111),correlation results (123), the context at the point of interaction,and/or other data, relevant and compelling messages or advertisementscan be selected for the customer at the points of interaction (e.g.,107) for targeted advertising. The messages or advertisements are thusdelivered at the optimal time for influencing or reinforcing brandperceptions and revenue-generating behavior. The customers receive theadvertisements in the media channels that they like and/or use mostfrequently.

In one embodiment, the transaction data (109) includes transactionamounts, the identities of the payees (e.g., merchants), and the dateand time of the transactions. The identities of the payees can becorrelated to the businesses, services, products and/or locations of thepayees. For example, the transaction handler (103) maintains a databaseof merchant data, including the merchant locations, businesses,services, products, etc. Thus, the transaction data (109) can be used todetermine the purchase behavior, pattern, preference, tendency,frequency, trend, budget and/or propensity of the customers in relationto various types of businesses, services and/or products and in relationto time.

In one embodiment, the products and/or services purchased by the user(101) are also identified by the information transmitted from themerchants or service providers. Thus, the transaction data (109) mayinclude identification of the individual products and/or services, whichallows the profile generator (121) to generate transaction profiles(127) with fine granularity or resolution. In one embodiment, thegranularity or resolution may be at a level of distinct products andservices that can be purchased (e.g., stock-keeping unit (SKU) level),or category or type of products or services, or vendor of products orservices, etc.

The profile generator (121) may consolidate transaction data for aperson having multiple accounts to derive intelligence information aboutthe person to generate a profile for the person (e.g., transactionprofiles (127), or the user specific profile (131)).

The profile generator (121) may consolidate transaction data for afamily having multiple accounts held by family members to deriveintelligence information about the family to generate a profile for thefamily (e.g., transaction profiles (127), or the user specific profile(131)).

Similarly, the profile generator (121) may consolidate transaction datafor a group of persons, after the group is identified by certaincharacteristics, such as gender, income level, geographical location orregion, preference, characteristics of past purchases (e.g., merchantcategories, purchase types), cluster, propensity, demographics, socialnetworking characteristics (e.g., relationships, preferences, activitieson social networking websites), etc. The consolidated transaction datacan be used to derive intelligence information about the group togenerate a profile for the group (e.g., transaction profiles (127), orthe user specific profile (131)).

In one embodiment, the profile generator (121) may consolidatetransaction data according to the user data (125) to generate a profilespecific to the user data (125).

Since the transaction data (109) are records and history of pastpurchases, the profile generator (121) can derive intelligenceinformation about a customer using an account, a customer using multipleaccounts, a family, a company, or other groups of customers, about whatthe targeted audience is likely to purchase in the future, howfrequently, and their likely budgets for such future purchases.Intelligence information is useful in selecting the advertisements thatare most useful, effective and compelling to the customer, thusincreasing the efficiency and effectiveness of the advertising process.

In one embodiment, the transaction data (109) are enhanced withcorrelation results (123) correlating past advertisements and purchasesresulting at least in part from the advertisements. Thus, theintelligence information can be more accurate in assisting with theselection of the advertisements. The intelligence information may notonly indicate what the audience is likely to purchase, but also howlikely the audience is to be influenced by advertisements for certainpurchases, and the relative effectiveness of different forms ofadvertisements for the audience. Thus, the advertisement selector (133)can select the advertisements to best use the opportunity to communicatewith the audience. Further, the transaction data (109) can be enhancedvia other data elements, such as program enrollment, affinity programs,redemption of reward points (or other types of offers), onlineactivities, such as web searches and web browsing, social networkinginformation, etc., based on the account data (111) and/or other data,such as non-transactional data discussed in U.S. patent application Ser.No. 12/614,603, filed Nov. 9, 2009 and entitled “Analyzing LocalNon-Transactional Data with Transactional Data in Predictive Models,”the disclosure of which is hereby incorporated herein by reference.

In one embodiment, the entity operating the transaction handler (103)provides the intelligence information in real-time as the request forthe intelligence information occurs. In other embodiments, the entityoperating the transaction handler (103) may provide the intelligenceinformation in batch mode. The intelligence information can be deliveredvia online communications (e.g., via an application programminginterface (API) on a website, or other information server), or viaphysical transportation of a computer readable media that stores thedata representing the intelligence information.

In one embodiment, the intelligence information is communicated tovarious entities in the system in a way similar to, and/or in parallelwith the information flow in the transaction system to move money. Thetransaction handler (103) routes the information in the same way itroutes the currency involved in the transactions.

In one embodiment, the portal (143) provides a user interface to allowthe user (101) to select items offered on different merchant websitesand store the selected items in a wish list for comparison, reviewing,purchasing, tracking, etc. The information collected via the wish listcan be used to improve the transaction profiles (127) and deriveintelligence on the needs of the user (101); and targeted advertisementscan be delivered to the user (101) via the wish list user interfaceprovided by the portal (143). Examples of user interface systems tomanage wish lists are provided in U.S. patent application Ser. No.12/683,802, filed Jan. 7, 2010 and entitled “System and Method forManaging Items of Interest Selected from Online Merchants,” thedisclosure of which is hereby incorporated herein by reference.

Aggregated Spending Profile

In one embodiment, the characteristics of transaction patterns ofcustomers are profiled via clusters, factors, and/or categories ofpurchases. The transaction data (109) may include transaction records(301); and in one embodiment, an aggregated spending profile (341) isgenerated from the transaction records (301), in a way illustrated inFIG. 6 , to summarize the spending behavior reflected in the transactionrecords (301).

In one embodiment, each of the transaction records (301) is for aparticular transaction processed by the transaction handler (103). Eachof the transaction records (301) provides information about theparticular transaction, such as the account number (312) of the consumeraccount (146) used to pay for the purchase, the date (303) (and/or time)of the transaction, the amount (314) of the transaction, the ID (305) ofthe merchant who receives the payment, the category (316) of themerchant, the channel (307) through which the purchase was made, etc.Examples of channels include online, offline in-store, via phone, etc.In one embodiment, the transaction records (301) may further include afield to identify a type of transaction, such as card-present,card-not-present, etc.

In one embodiment, a “card-present” transaction involves physicallypresenting the account identification device (141), such as a financialtransaction card, to the merchant (e.g., via swiping a credit card at aPOS terminal of a merchant); and a “card-not-present” transactioninvolves presenting the account information (142) of the consumeraccount (146) to the merchant to identify the consumer account (146)without physically presenting the account identification device (141) tothe merchant or the transaction terminal (105).

In one embodiment, certain information about the transaction can belooked up in a separate database based on other information recorded forthe transaction. For example, a database may be used to storeinformation about merchants, such as the geographical locations of themerchants, categories of the merchants, etc. Thus, the correspondingmerchant information related to a transaction can be determined usingthe merchant ID (305) recorded for the transaction.

In one embodiment, the transaction records (301) may further includedetails about the products and/or services involved in the purchase. Forexample, a list of items purchased in the transaction may be recordedtogether with the respective purchase prices of the items and/or therespective quantities of the purchased items. The products and/orservices can be identified via stock-keeping unit (SKU) numbers, orproduct category IDs. The purchase details may be stored in a separatedatabase and be looked up based on an identifier of the transaction.

When there is voluminous data representing the transaction records(301), the spending patterns reflected in the transaction records (301)can be difficult to recognize by an ordinary person.

In one embodiment, the voluminous transaction records (301) aresummarized (335) into aggregated spending profiles (e.g., 341) toconcisely present the statistical spending characteristics reflected inthe transaction records (301). The aggregated spending profile (341)uses values derived from statistical analysis to present the statisticalcharacteristics of transaction records (301) of an entity in a way easyto understand by an ordinary person.

In FIG. 6 , the transaction records (301) are summarized (335) viafactor analysis (327) to condense the variables (e.g., 313, 315) and viacluster analysis (329) to segregate entities by spending patterns.

In FIG. 6 , a set of variables (e.g., 311, 313, 315) are defined basedon the parameters recorded in the transaction records (301). Thevariables (e.g., 311, 313, and 315) are defined in a way to havemeanings easily understood by an ordinary person. For example, variables(311) measure the aggregated spending in super categories; variables(313) measure the spending frequencies in various areas; and variables(315) measure the spending amounts in various areas. In one embodiment,each of the areas is identified by a merchant category (316) (e.g., asrepresented by a merchant category code (MCC), a North American IndustryClassification System (NAICS) code, or a similarly standardized categorycode). In other embodiments, an area may be identified by a productcategory, a SKU number, etc.

In one embodiment, a variable of a same category (e.g., frequency (313)or amount (315)) is defined to be aggregated over a set of mutuallyexclusive areas. A transaction is classified in only one of the mutuallyexclusive areas. For example, in one embodiment, the spending frequencyvariables (313) are defined for a set of mutually exclusive merchants ormerchant categories. Transactions falling with the same category areaggregated.

Examples of the spending frequency variables (313) and spending amountvariables (315) defined for various merchant categories (e.g., 316) inone embodiment are provided in U.S. patent application Ser. No.12/537,566, filed Aug. 7, 2009 and entitled “Cardholder Clusters,” thedisclosure of which application is hereby incorporated herein byreference.

In one embodiment, super categories (311) are defined to group thecategories (e.g., 316) used in transaction records (301). The supercategories (311) can be mutually exclusive. For example, each merchantcategory (316) is classified under only one super merchant category butnot any other super merchant categories. Since the generation of thelist of super categories typically requires deep domain knowledge aboutthe businesses of the merchants in various categories, super categories(311) are not used in one embodiment.

In one embodiment, the aggregation (317) includes the application of thedefinitions (309) for these variables (e.g., 311, 313, and 315) to thetransaction records (301) to generate the variable values (321). Thetransaction records (301) are aggregated to generate aggregatedmeasurements (e.g., variable values (321)) that are not specific to aparticular transaction, such as frequencies of purchases made withdifferent merchants or different groups of merchants, the amounts spentwith different merchants or different groups of merchants, and thenumber of unique purchases across different merchants or differentgroups of merchants, etc. The aggregation (317) can be performed for aparticular time period and for entities at various levels.

In one embodiment, the transaction records (301) are aggregatedaccording to a buying entity. The aggregation (317) can be performed ataccount level, person level, family level, company level, neighborhoodlevel, city level, region level, etc. to analyze the spending patternsacross various areas (e.g., sellers, products or services) for therespective aggregated buying entity. For example, the transactionrecords (301) for a particular account (e.g., presented by the accountnumber (312)) can be aggregated for an account level analysis. Toaggregate the transaction records (301) in account level, thetransactions with a specific merchant or merchants in a specificcategory are counted according to the variable definitions (309) for aparticular account to generate a frequency measure (e.g., 313) for theaccount relative to the specific merchant or merchant category; and thetransaction amounts (e.g., 314) with the specific merchant or thespecific category of merchants are summed for the particular account togenerate an average spending amount for the account relative to thespecific merchant or merchant category. For example, the transactionrecords (301) for a particular person having multiple accounts can beaggregated for a person level analysis, the transaction records (301)aggregated for a particular family for a family level analysis, and thetransaction records (301) for a particular business aggregated for abusiness level analysis.

The aggregation (317) can be performed for a predetermined time period,such as for the transactions occurring in the past month, in the pastthree months, in the past twelve months, etc.

In another embodiment, the transaction records (301) are aggregatedaccording to a selling entity. The spending patterns at the sellingentity across various buyers, products or services can be analyzed. Forexample, the transaction records (301) for a particular merchant havingtransactions with multiple accounts can be aggregated for a merchantlevel analysis. For example, the transaction records (301) for aparticular merchant group can be aggregated for a merchant group levelanalysis.

In one embodiment, the aggregation (317) is formed separately fordifferent types of transactions, such as transactions made online,offline, via phone, and/or “card-present”transactions vs.“card-not-present” transactions, which can be used to identify thespending pattern differences among different types of transactions.

In one embodiment, the variable values (e.g., 323, 324, . . . , 325)associated with an entity ID (322) are considered the random samples ofthe respective variables (e.g., 311, 313, 315), sampled for the instanceof an entity represented by the entity ID (322). Statistical analyses(e.g., factor analysis (327) and cluster analysis (329)) are performedto identify the patterns and correlations in the random samples.

For example, a cluster analysis (329) can identify a set of clusters andthus cluster definitions (333) (e.g., the locations of the centroids ofthe clusters). In one embodiment, each entity ID (322) is represented asa point in a mathematical space defined by the set of variables; and thevariable values (323, 324, . . . , 325) of the entity ID (322) determinethe coordinates of the point in the space and thus the location of thepoint in the space. Various points may be concentrated in variousregions; and the cluster analysis (329) is configured to formulate thepositioning of the points to drive the clustering of the points. Inother embodiments, the cluster analysis (329) can also be performedusing the techniques of Self Organizing Maps (SOM), which can identifyand show clusters of multi-dimensional data using a representation on atwo-dimensional map.

Once the cluster definitions (333) are obtained from the clusteranalysis (329), the identity of the cluster (e.g., cluster ID (343))that contains the entity ID (322) can be used to characterize spendingbehavior of the entity represented by the entity ID (322). The entitiesin the same cluster are considered to have similar spending behaviors.

Similarities and differences among the entities, such as accounts,individuals, families, etc., as represented by the entity ID (e.g., 322)and characterized by the variable values (e.g., 323, 324, . . . , 325)can be identified via the cluster analysis (329). In one embodiment,after a number of clusters of entity IDs are identified based on thepatterns of the aggregated measurements, a set of profiles can begenerated for the clusters to represent the characteristics of theclusters. Once the clusters are identified, each of the entity IDs(e.g., corresponding to an account, individual, family) can be assignedto one cluster; and the profile for the corresponding cluster may beused to represent, at least in part, the entity (e.g., account,individual, family). Alternatively, the relationship between an entity(e.g., an account, individual, family) and one or more clusters can bedetermined (e.g., based on a measurement of closeness to each cluster).Thus, the cluster related data can be used in a transaction profile (127or 341) to provide information about the behavior of the entity (e.g.,an account, an individual, a family).

In one embodiment, more than one set of cluster definitions (333) isgenerated from cluster analyses (329). For example, cluster analyses(329) may generate different sets of cluster solutions corresponding todifferent numbers of identified clusters. A set of cluster IDs (e.g.,343) can be used to summarize (335) the spending behavior of the entityrepresented by the entity ID (322), based on the typical spendingbehavior of the respective clusters. In one example, two clustersolutions are obtained; one of the cluster solutions has 17 clusters,which classify the entities in a relatively coarse manner; and the othercluster solution has 55 clusters, which classify the entities in arelative fine manner. A cardholder can be identified by the spendingbehavior of one of the 17 clusters and one of the 55 clusters in whichthe cardholder is located. Thus, the set of cluster IDs corresponding tothe set of cluster solutions provides a hierarchical identification ofan entity among clusters of different levels of resolution. The spendingbehavior of the clusters is represented by the cluster definitions(333), such as the parameters (e.g., variable values) that define thecentroids of the clusters.

In one embodiment, the random variables (e.g., 313 and 315) as definedby the definitions (309) have certain degrees of correlation and are notindependent from each other. For example, merchants of differentmerchant categories (e.g., 316) may have overlapping business, or havecertain business relationships. For example, certain products and/orservices of certain merchants have cause and effect relationships. Forexample, certain products and/or services of certain merchants aremutually exclusive to a certain degree (e.g., a purchase from onemerchant may have a level of probability to exclude the user (101) frommaking a purchase from another merchant). Such relationships may becomplex and difficult to quantify by merely inspecting the categories.Further, such relationships may shift over time as the economy changes.

In one embodiment, a factor analysis (327) is performed to reduce theredundancy and/or correlation among the variables (e.g., 313, 315). Thefactor analysis (327) identifies the definitions (331) for factors, eachof which represents a combination of the variables (e.g., 313, 315).

In one embodiment, a factor is a linear combination of a plurality ofthe aggregated measurements (e.g., variables (313, 315)) determined forvarious areas (e.g., merchants or merchant categories, products orproduct categories). Once the relationship between the factors and theaggregated measurements is determined via factor analysis, the valuesfor the factors can be determined from the linear combinations of theaggregated measurements and be used in a transaction profile (127 or341) to provide information on the behavior of the entity represented bythe entity ID (e.g., an account, an individual, a family).

Once the factor definitions (331) are obtained from the factor analysis(327), the factor definitions (331) can be applied to the variablevalues (321) to determine factor values (344) for the aggregatedspending profile (341). Since redundancy and correlation are reduced inthe factors, the number of factors is typically much smaller than thenumber of the original variables (e.g., 313, 315). Thus, the factorvalues (344) represent the concise summary of the original variables(e.g., 313, 315).

For example, there may be thousands of variables on spending frequencyand amount for different merchant categories; and the factor analysis(327) can reduce the factor number to less than one hundred (and evenless than twenty). In one example, a twelve-factor solution is obtained,which allows the use of twelve factors to combine the thousands of theoriginal variables (313, 315); and thus, the spending behavior inthousands of merchant categories can be summarized via twelve factorvalues (344). In one embodiment, each factor is combination of at leastfour variables; and a typical variable has contributions to more thanone factor.

In one example, hundreds or thousands of transaction records (301) of acardholder are converted into hundreds or thousands of variable values(321) for various merchant categories, which are summarized (335) viathe factor definitions (331) and cluster definitions (333) into twelvefactor values (344) and one or two cluster IDs (e.g., 343). Thesummarized data can be readily interpreted by a human to ascertain thespending behavior of the cardholder. A user (101) may easily specify aspending behavior requirement formulated based on the factor values(344) and the cluster IDs (e.g., to query for a segment of customers, orto request the targeting of a segment of customers). The reduced size ofthe summarized data reduces the need for data communication bandwidthfor communicating the spending behavior of the cardholder over a networkconnection and allows simplified processing and utilization of the datarepresenting the spending behavior of the cardholder.

In one embodiment, the behavior and characteristics of the clusters arestudied to identify a description of a type of representative entitiesthat are found in each of the clusters. The clusters can be named basedon the type of representative entities to allow an ordinary person toeasily understand the typical behavior of the clusters.

In one embodiment, the behavior and characteristics of the factors arealso studied to identify dominant aspects of each factor. The clusterscan be named based on the dominant aspects to allow an ordinary personto easily understand the meaning of a factor value.

In FIG. 6 , an aggregated spending profile (341) for an entityrepresented by an entity ID (e.g., 322) includes the cluster ID (343)and factor values (344) determined based on the cluster definitions(333) and the factor definitions (331). The aggregated spending profile(341) may further include other statistical parameters, such asdiversity index (342), channel distribution (345), category distribution(346), zip code (347), etc., as further discussed below.

In one embodiment, the diversity index (342) may include an entropyvalue and/or a Gini coefficient, to represent the diversity of thespending by the entity represented by the entity ID (322) acrossdifferent areas (e.g., different merchant categories (e.g., 316)). Whenthe diversity index (342) indicates that the diversity of the spendingdata is under a predetermined threshold level, the variable values(e.g., 323, 324, . . . , 325) for the corresponding entity ID (322) maybe excluded from the cluster analysis (329) and/or the factor analysis(327) due to the lack of diversity. When the diversity index (342) ofthe aggregated spending profile (341) is lower than a predeterminedthreshold, the factor values (344) and the cluster ID (343) may notaccurately represent the spending behavior of the corresponding entity.

In one embodiment, the channel distribution (345) includes a set ofpercentage values that indicate the percentages of amounts spent indifferent purchase channels, such as online, via phone, in a retailstore, etc.

In one embodiment, the category distribution (346) includes a set ofpercentage values that indicate the percentages of spending amounts indifferent super categories (311). In one embodiment, thousands ofdifferent merchant categories (e.g., 316) are represented by MerchantCategory Codes (MCC), or North American Industry Classification System(NAICS) codes in transaction records (301). These merchant categories(e.g., 316) are classified or combined into less than one hundred supercategories (or less than twenty). In one example, fourteen supercategories are defined based on domain knowledge.

In one embodiment, the aggregated spending profile (341) includes theaggregated measurements (e.g., frequency, average spending amount)determined for a set of predefined, mutually exclusive merchantcategories (e.g., super categories (311)). Each of the super merchantcategories represents a type of products or services a customer maypurchase. A transaction profile (127 or 341) may include the aggregatedmeasurements for each of the set of mutually exclusive merchantcategories. The aggregated measurements determined for the predefined,mutually exclusive merchant categories can be used in transactionprofiles (127 or 341) to provide information on the behavior of arespective entity (e.g., an account, an individual, or a family).

In one embodiment, the zip code (347) in the aggregated spending profile(341) represents the dominant geographic area in which the spendingassociated with the entity ID (322) occurred. Alternatively or incombination, the aggregated spending profile (341) may include adistribution of transaction amounts over a set of zip codes that accountfor a majority of the transactions or transaction amounts (e.g., 90%).

In one embodiment, the factor analysis (327) and cluster analysis (329)are used to summarize the spending behavior across various areas, suchas different merchants characterized by merchant category (316),different products and/or services, different consumers, etc. Theaggregated spending profile (341) may include more or fewer fields thanthose illustrated in FIG. 6 . For example, in one embodiment, theaggregated spending profile (341) further includes an aggregatedspending amount for a period of time (e.g., the past twelve months); inanother embodiment, the aggregated spending profile (341) does notinclude the category distribution (346); and in a further embodiment,the aggregated spending profile (341) may include a set of distancemeasures to the centroids of the clusters. The distance measures may bedefined based on the variable values (323, 324, . . . , 325), or basedon the factor values (344). The factor values of the centroids of theclusters may be estimated based on the entity ID (e.g., 322) that isclosest to the centroid in the respective cluster.

Other variables can be used in place of, or in additional to, thevariables (311, 313, 315) illustrated in FIG. 6 . For example, theaggregated spending profile (341) can be generated using variablesmeasuring shopping radius/distance from the primary address of theaccount holder to the merchant site for offline purchases. When suchvariables are used, the transaction patterns can be identified based atleast in part on clustering according to shopping radius/distance andgeographic regions. Similarly, the factor definition (331) may includethe consideration of the shopping radius/distance. For example, thetransaction records (301) may be aggregated based on the ranges ofshopping radius/distance and/or geographic regions. For example, thefactor analysis can be used to determine factors that naturally combinegeographical areas based on the correlations in the spending patterns invarious geographical areas.

In one embodiment, the aggregation (317) may involve the determinationof a deviation from a trend or pattern. For example, an account makes acertain number of purchases a week at a merchant over the past 6 months.However, in the past 2 weeks the number of purchases is less than theaverage number per week. A measurement of the deviation from the trendor pattern can be used (e.g., in a transaction profile (127 or 341) as aparameter, or in variable definitions (309) for the factor analysis(327) and/or the cluster analysis) to define the behavior of an account,an individual, a family, etc.

FIG. 7 shows a method to generate an aggregated spending profileaccording to one embodiment. In FIG. 7 , computation models areestablished (351) for variables (e.g., 311, 313, and 315). In oneembodiment, the variables are defined in a way to capture certainaspects of the spending statistics, such as frequency, amount, etc.

In FIG. 7 , data from related accounts are combined (353). For example,when an account number change has occurred for a cardholder in the timeperiod under analysis, the transaction records (301) under the differentaccount numbers of the same cardholder are combined under one accountnumber that represents the cardholder. For example, when the analysis isperformed at a person level (or family level, business level, socialgroup level, city level, or region level), the transaction records (301)in different accounts of the person (or family, business, social group,city or region) can be combined under one entity ID (322) thatrepresents the person (or family, business, social group, city orregion).

In one embodiment, recurrent/installment transactions are combined(355). For example, multiple monthly payments may be combined andconsidered as one single purchase.

In FIG. 7 , account data are selected (357) according to a set ofcriteria related to activity, consistency, diversity, etc.

For example, when a cardholder uses a credit card solely to purchasegas, the diversity of the transactions by the cardholder is low. In sucha case, the transactions in the account of the cardholder may not bestatistically meaningful to represent the spending pattern of thecardholder in various merchant categories. Thus, in one embodiment, ifthe diversity of the transactions associated with an entity ID (322) isbelow a threshold, the variable values (e.g., 323, 324, . . . , 325)corresponding to the entity ID (322) are not used in the clusteranalysis (329) and/or the factor analysis (327). The diversity can beexamined based on the diversity index (342) (e.g., entropy or Ginicoefficient), or based on counting the different merchant categories inthe transactions associated with the entity ID (322); and when the countof different merchant categories is fewer than a threshold (e.g., 5),the transactions associated with the entity ID (322) are not used in thecluster analysis (329) and/or the factor analysis (327) due to the lackof diversity.

For example, when a cardholder uses a credit card only sporadically(e.g., when running out of cash), the limited transactions by thecardholder may not be statistically meaningful in representing thespending behavior of the cardholder. Thus, in one embodiment, when thenumbers of transactions associated with an entity ID (322) is below athreshold, the variable values (e.g., 323, 324, . . . , 325)corresponding to the entity ID (322) are not used in the clusteranalysis (329) and/or the factor analysis (327).

For example, when a cardholder has only used a credit card during aportion of the time period under analysis, the transaction records (301)during the time period may not reflect the consistent behavior of thecardholder for the entire time period. Consistency can be checked invarious ways. In one example, if the total number of transactions duringthe first and last months of the time period under analysis is zero, thetransactions associated with the entity ID (322) are inconsistent in thetime period and thus are not used in the cluster analysis (329) and/orthe factor analysis (327). Other criteria can be formulated to detectinconsistency in the transactions.

In FIG. 7 , the computation models (e.g., as represented by the variabledefinitions (309)) are applied (359) to the remaining account data(e.g., transaction records (301)) to obtain data samples for thevariables. The data points associated with the entities, other thanthose whose transactions fail to meet the minimum requirements foractivity, consistency, diversity, etc., are used in factor analysis(327) and cluster analysis (329).

In FIG. 7 , the data samples (e.g., variable values (321)) are used toperform (361) factor analysis (327) to identify factor solutions (e.g.,factor definitions (331)). The factor solutions can be adjusted (363) toimprove similarity in factor values of different sets of transactiondata (109). For example, factor definitions (331) can be applied to thetransactions in the time period under analysis (e.g., the past twelvemonths) and be applied separately to the transactions in a prior timeperiod (e.g., the twelve months before the past twelve months) to obtaintwo sets of factor values. The factor definitions (331) can be adjustedto improve the correlation between the two set of factor values.

The data samples can also be used to perform (365) cluster analysis(329) to identify cluster solutions (e.g., cluster definitions (333)).The cluster solutions can be adjusted (367) to improve similarity incluster identifications based on different sets of transaction data(109). For example, cluster definitions (333) can be applied to thetransactions in the time period under analysis (e.g., the past twelvemonths) and be applied separately to the transactions in a prior timeperiod (e.g., the twelve months before the past twelve months) to obtaintwo sets of cluster identifications for various entities. The clusterdefinitions (333) can be adjusted to improve the correlation between thetwo set of cluster identifications.

In one embodiment, the number of clusters is determined from clusteringanalysis. For example, a set of cluster seeds can be initiallyidentified and used to run a known clustering algorithm. The sizes ofdata points in the clusters are then examined. When a cluster containsless than a predetermined number of data points, the cluster may beeliminated to rerun the clustering analysis.

In one embodiment, standardizing entropy is added to the clustersolution to obtain improved results.

In one embodiment, human understandable characteristics of the factorsand clusters are identified (369) to name the factors and clusters. Forexample, when the spending behavior of a cluster appears to be thebehavior of an internet loyalist, the cluster can be named “internetloyalist” such that if a cardholder is found to be in the “internetloyalist” cluster, the spending preferences and patterns of thecardholder can be easily perceived.

In one embodiment, the factor analysis (327) and the cluster analysis(329) are performed periodically (e.g., once a year, or six months) toupdate the factor definitions (331) and the cluster definitions (333),which may change as the economy and the society change over time.

In FIG. 7 , transaction data (109) are summarized (371) using the factorsolutions and cluster solutions to generate the aggregated spendingprofile (341). The aggregated spending profile (341) can be updated morefrequently than the factor solutions and cluster solutions, when the newtransaction data (109) becomes available. For example, the aggregatedspending profile (341) may be updated quarterly or monthly.

Various tweaks and adjustments can be made for the variables (e.g., 313,315) used for the factor analysis (327) and the cluster analysis (329).For example, the transaction records (301) may be filtered, weighted orconstrained, according to different rules to improve the capabilities ofthe aggregated measurements in indicating certain aspects of thespending behavior of the customers.

For example, in one embodiment, the variables (e.g., 313, 315) arenormalized and/or standardized (e.g., using statistical average, mean,and/or variance).

For example, the variables (e.g., 313, 315) for the aggregatedmeasurements can be tuned, via filtering and weighting, to predict thefuture trend of spending behavior (e.g., for advertisement selection),to identify abnormal behavior (e.g., for fraud prevention), or toidentify a change in spending pattern (e.g., for advertisement audiencemeasurement), etc. The aggregated measurements, the factor values (344),and/or the cluster ID (343) generated from the aggregated measurementscan be used in a transaction profile (127 or 341) to define the behaviorof an account, an individual, a family, etc.

In one embodiment, the transaction data (109) are aged to provide moreweight to recent data than older data. In other embodiments, thetransaction data (109) are reverse aged. In further embodiments, thetransaction data (109) are seasonally adjusted.

In one embodiment, the variables (e.g., 313, 315) are constrained toeliminate extreme outliers. For example, the minimum values and themaximum values of the spending amounts (315) may be constrained based onvalues at certain percentiles (e.g., the value at one percentile as theminimum and the value at 99 percentile as the maximum) and/or certainpredetermined values. In one embodiment, the spending frequencyvariables (313) are constrained based on values at certain percentilesand median values. For example, the minimum value for a spendingfrequency variable (313) may be constrained at P₁ ^(˜)k×(M^(˜)P₁), whereP₁ is the one percentile value, M the median value, and k apredetermined constant (e.g., 0.1). For example, the maximum value for aspending frequency variable (313) may be constrained at P₉₉ a×(P₉₉^(˜)M), where P₉₉ is the 99 percentile value, M the median value, and ka predetermined constant (e.g., 0.1).

In one embodiment, variable pruning is performed to reduce the number ofvariables (e.g., 313, 315) that have less impact on cluster solutionsand/or factor solutions. For example, variables with standard variationless than a predetermined threshold (e.g., 0.1) may be discarded for thepurpose of cluster analysis (329). For example, analysis of variance(ANOVA) can be performed to identify and remove variables that are nomore significant than a predetermined threshold.

The aggregated spending profile (341) can provide information onspending behavior for various application areas, such as marketing,fraud detection and prevention, creditworthiness assessment, loyaltyanalytics, targeting of offers, etc.

For example, clusters can be used to optimize offers for various groupswithin an advertisement campaign. The use of factors and clusters totarget advertisement can improve the speed of producing targetingmodels. For example, using variables based on factors and clusters (andthus eliminating the need to use a large number of convention variables)can improve predictive models and increase efficiency of targeting byreducing the number of variables examined. The variables formulatedbased on factors and/or clusters can be used with other variables tobuild predictive models based on spending behaviors.

In one embodiment, the aggregated spending profile (341) can be used tomonitor risks in transactions. Factor values are typically consistentover time for each entity. An abrupt change in some of the factor valuesmay indicate a change in financial conditions, or a fraudulent use ofthe account. Models formulated using factors and clusters can be used toidentify a series of transactions that do not follow a normal patternspecified by the factor values (344) and/or the cluster ID (343).Potential bankruptcies can be predicted by analyzing the change offactor values over time; and significant changes in spending behaviormay be detected to stop and/or prevent fraudulent activities.

For example, the factor values (344) can be used in regression modelsand/or neural network models for the detection of certain behaviors orpatterns. Since factors are relatively non-collinear, the factors canwork well as independent variables. For example, factors and clusterscan be used as independent variables in tree models.

For example, surrogate accounts can be selected for the construction ofa quasi-control group. For example, for a given account A that is in onecluster, the account B that is closest to the account A in the samecluster can be selected as a surrogate account of the account B. Thecloseness can be determined by certain values in the aggregated spendingprofile (341), such as factor values (344), category distribution (346),etc. For example, a Euclidian distance defined based on the set ofvalues from the aggregated spending profile (341) can be used to comparethe distances between the accounts. Once identified, the surrogateaccount can be used to reduce or eliminate bias in measurements. Forexample, to determine the effect of an advertisement, the spendingpattern response of the account A that is exposed to the advertisementcan be compared to the spending pattern response of the account B thatis not exposed to the advertisement.

For example, the aggregated spending profile (341) can be used insegmentation and/or filtering analysis, such as selecting cardholdershaving similar spending behaviors identified via factors and/or clustersfor targeted advertisement campaigns, and selecting and determining agroup of merchants that could be potentially marketed towardscardholders originating in a given cluster (e.g., for bundled offers).For example, a query interface can be provided to allow the query toidentify a targeted population based on a set of criteria formulatedusing the values of clusters and factors.

For example, the aggregated spending profile (341) can be used in aspending comparison report, such as comparing a sub-population ofinterest against the overall population, determining how clusterdistributions and mean factor values differ, and building reports formerchants and/or issuers for benchmarking purposes. For example, reportscan be generated according to clusters in an automated way for themerchants. For example, the aggregated spending profile (341) can beused in geographic reports by identifying geographic areas wherecardholders shop most frequently and comparing predominant spendinglocations with cardholder residence locations.

In one embodiment, the profile generator (121) provides affinityrelationship data in the transaction profiles (127) so that thetransaction profiles (127) can be shared with business partners withoutcompromising the privacy of the users (101) and the transaction details.

For example, in one embodiment, the profile generator (121) is toidentify clusters of entities (e.g., accounts, cardholders, families,businesses, cities, regions, etc.) based on the spending patterns of theentities. The clusters represent entity segments identified based on thespending patterns of the entities reflected in the transaction data(109) or the transaction records (301).

In one embodiment, the clusters correspond to cells or regions in themathematical space that contain the respective groups of entities. Forexample, the mathematical space representing the characteristics ofusers (101) may be divided into clusters (cells or regions). Forexample, the cluster analysis (329) may identify one cluster in the cellor region that contains a cluster of entity IDs (e.g., 322) in the spacehaving a plurality of dimensions corresponding to the variables (e.g.,313 and 315). For example, a cluster can also be identified as a cell orregion in a space defined by the factors using the factor definitions(331) generated from the factor analysis (327).

In one embodiment, the parameters used in the aggregated spendingprofile (341) can be used to define a segment or a cluster of entities.For example, a value for the cluster ID (343) and a set of ranges forthe factor values (344) and/or other values can be used to define asegment.

In one embodiment, a set of clusters are standardized to represent thepredilection of entities in various groups for certain products orservices. For example, a set of standardized clusters can be formulatedfor people who have shopped, for example, at home improvement stores.The cardholders in the same cluster have similar spending behavior.

In one embodiment, the tendency or likelihood of a user (101) being in aparticular cluster (i.e. the user's affinity to the cell) can becharacterized using a value, based on past purchases. The same user(101) may have different affinity values for different clusters.

For example, a set of affinity values can be computed for an entity,based on the transaction records (301), to indicate the closeness orpredilection of the entity to the set of standardized clusters. Forexample, a cardholder who has a first value representing affinity of thecardholder to a first cluster may have a second value representingaffinity of the cardholder to a second cluster. For example, if aconsumer buys a lot of electronics, the affinity value of the consumerto the electronics cluster is high.

In one embodiment, other indicators are formulated across the merchantcommunity and cardholder behavior and provided in the profile (e.g., 127or 341) to indicate the risk of a transaction.

In one embodiment, the relationship of a pair of values from twodifferent clusters provides an indication of the likelihood that theuser (101) is in one of the two cells, if the user (101) is shown to bein the other cell. For example, if the likelihood of the user (101) topurchase each of two types of products is known, the scores can be usedto determine the likelihood of the user (101) buying one of the twotypes of products if the user (101) is known to be interested in theother type of products. In one embodiment, a map of the values for theclusters is used in a profile (e.g., 127 or 341) to characterize thespending behavior of the user (101) (or other types of entities, such asa family, company, neighborhood, city, or other types of groups definedby other aggregate parameters, such as time of day, etc.).

In one embodiment, the clusters and affinity information arestandardized to allow sharing between business partners, such astransaction processing organizations, search providers, and marketers.Purchase statistics and search statistics are generally described indifferent ways. For example, purchase statistics are based on merchants,merchant categories, SKU numbers, product descriptions, etc.; and searchstatistics are based on search terms. Once the clusters arestandardized, the clusters can be used to link purchase informationbased merchant categories (and/or SKU numbers, product descriptions)with search information based on search terms. Thus, search predilectionand purchase predilection can be mapped to each other.

In one embodiment, the purchase data and the search data (or other thirdparty data) are correlated based on mapping to the standardized clusters(cells or segments). The purchase data and the search data (or otherthird party data) can be used together to provide benefits or offers(e.g., coupons) to consumers. For example, standardized clusters can beused as a marketing tool to provide relevant benefits, includingcoupons, statement credits, or the like to consumers who are within orare associated with common clusters. For example, a data exchangeapparatus may obtain cluster data based on consumer search engine dataand actual payment transaction data to identify like groups ofindividuals who may respond favorably to particular types of benefits,such as coupons and statement credits.

Details about aggregated spending profile (341) in one embodiment areprovided in U.S. patent application Ser. No. 12/777,173, filed May 10,2010 and entitled “Systems and Methods to Summarize Transaction Data,”the disclosure of which is hereby incorporated herein by reference.

Transaction Data Based Portal

In FIG. 5 , the transaction terminal (105) initiates the transaction fora user (101) (e.g., a customer) for processing by a transaction handler(103). The transaction handler (103) processes the transaction andstores transaction data (109) about the transaction, in connection withaccount data (111), such as the account profile of an account of theuser (101). The account data (111) may further include data about theuser (101), collected from issuers or merchants, and/or other sources,such as social networks, credit bureaus, merchant provided information,address information, etc. In one embodiment, a transaction may beinitiated by a server (e.g., based on a stored schedule for recurrentpayments).

Over a period of time, the transaction handler (103) accumulates thetransaction data (109) from transactions initiated at differenttransaction terminals (e.g., 105) for different users (e.g., 101). Thetransaction data (109) thus includes information on purchases made byvarious users (e.g., 101) at various times via different purchasesoptions (e.g., online purchase, offline purchase from a retail store,mail order, order via phone, etc.)

In one embodiment, the accumulated transaction data (109) and thecorresponding account data (111) are used to generate intelligenceinformation about the purchase behavior, pattern, preference, tendency,frequency, trend, amount and/or propensity of the users (e.g., 101), asindividuals or as a member of a group. The intelligence information canthen be used to generate, identify and/or select targeted advertisementsfor presentation to the user (101) on the point of interaction (107),during a transaction, after a transaction, or when other opportunitiesarise.

FIG. 8 shows a system to provide information based on transaction data(109) according to one embodiment. In FIG. 8 , the transaction handler(103) is coupled between an issuer processor (145) and an acquirerprocessor (147) to facilitate authorization and settlement oftransactions between a consumer account (146) and a merchant account(148). The transaction handler (103) records the transactions in thedata warehouse (149). The portal (143) is coupled to the data warehouse(149) to provide information based on the transaction records (301),such as the transaction profiles (127) or aggregated spending profile(341). The portal (143) may be implemented as a web portal, a telephonegateway, a file/data server, etc.

In one embodiment, the portal (143) is configured to provideinformation, such as transaction profiles (127) to third parties.Further, the portal (143) may register certain users (101) for variousprograms, such as a loyalty program to provide rewards and/or offers tothe users (101).

In one embodiment, the portal (143) is to register the interest of users(101), or to obtain permissions from the users (101) to gather furtherinformation about the users (101), such as data capturing purchasedetails, online activities, etc.

In one embodiment, the user (101) may register via the issuer; and theregistration data in the consumer account (146) may propagate to thedata warehouse (149) upon approval from the user (101).

In one embodiment, the portal (143) is to register merchants and provideservices and/or information to merchants.

In one embodiment, the portal (143) is to receive information from thirdparties, such as search engines, merchants, web sites, etc. The thirdparty data can be correlated with the transaction data (109) to identifythe relationships between purchases and other events, such as searches,news announcements, conferences, meetings, etc., and improve theprediction capability and accuracy.

In FIG. 8 , the consumer account (146) is under the control of theissuer processor (145). The consumer account (146) may be owned by anindividual, or an organization such as a business, a school, etc. Theconsumer account (146) may be a credit account, a debit account, or astored value account. The issuer may provide the consumer (e.g., user(101)) an account identification device (141) to identify the consumeraccount (146) using the account information (142). The respectiveconsumer of the account (146) can be called an account holder or acardholder, even when the consumer is not physically issued a card, orthe account identification device (141), in one embodiment. The issuerprocessor (145) is to charge the consumer account (146) to pay forpurchases.

In one embodiment, the account identification device (141) is a plasticcard having a magnetic strip storing account information (142)identifying the consumer account (146) and/or the issuer processor(145). Alternatively, the account identification device (141) is asmartcard having an integrated circuit chip storing at least the accountinformation (142). In one embodiment, the account identification device(141) includes a mobile phone having an integrated smartcard.

In one embodiment, the account information (142) is printed or embossedon the account identification device (141). The account information(142) may be printed as a bar code to allow the transaction terminal(105) to read the information via an optical scanner. The accountinformation (142) may be stored in a memory of the accountidentification device (141) and configured to be read via wireless,contactless communications, such as near field communications viamagnetic field coupling, infrared communications, or radio frequencycommunications. Alternatively, the transaction terminal (105) mayrequire contact with the account identification device (141) to read theaccount information (142) (e.g., by reading the magnetic strip of a cardwith a magnetic strip reader).

In one embodiment, the transaction terminal (105) is configured totransmit an authorization request message to the acquirer processor(147). The authorization request includes the account information (142),an amount of payment, and information about the merchant (e.g., anindication of the merchant account (148)). The acquirer processor (147)requests the transaction handler (103) to process the authorizationrequest, based on the account information (142) received in thetransaction terminal (105). The transaction handler (103) routes theauthorization request to the issuer processor (145) and may process andrespond to the authorization request when the issuer processor (145) isnot available. The issuer processor (145) determines whether toauthorize the transaction based at least in part on a balance of theconsumer account (146).

In one embodiment, the transaction handler (103), the issuer processor(145), and the acquirer processor (147) may each include a subsystem toidentify the risk in the transaction and may reject the transactionbased on the risk assessment.

In one embodiment, the account identification device (141) includessecurity features to prevent unauthorized uses of the consumer account(146), such as a logo to show the authenticity of the accountidentification device (141), encryption to protect the accountinformation (142), etc.

In one embodiment, the transaction terminal (105) is configured tointeract with the account identification device (141) to obtain theaccount information (142) that identifies the consumer account (146)and/or the issuer processor (145). The transaction terminal (105)communicates with the acquirer processor (147) that controls themerchant account (148) of a merchant. The transaction terminal (105) maycommunicate with the acquirer processor (147) via a data communicationconnection, such as a telephone connection, an Internet connection, etc.The acquirer processor (147) is to collect payments into the merchantaccount (148) on behalf of the merchant.

In one embodiment, the transaction terminal (105) is a POS terminal at atraditional, offline, “brick and mortar” retail store. In anotherembodiment, the transaction terminal (105) is an online server thatreceives account information (142) of the consumer account (146) fromthe user (101) through a web connection. In one embodiment, the user(101) may provide account information (142) through a telephone call,via verbal communications with a representative of the merchant; and therepresentative enters the account information (142) into the transactionterminal (105) to initiate the transaction.

In one embodiment, the account information (142) can be entered directlyinto the transaction terminal (105) to make payment from the consumeraccount (146), without having to physically present the accountidentification device (141). When a transaction is initiated withoutphysically presenting an account identification device (141), thetransaction is classified as a “card-not-present” (CNP) transaction.

In one embodiment, the issuer processor (145) may control more than oneconsumer account (146); the acquirer processor (147) may control morethan one merchant account (148); and the transaction handler (103) isconnected between a plurality of issuer processors (e.g., 145) and aplurality of acquirer processors (e.g., 147). An entity (e.g., bank) mayoperate both an issuer processor (145) and an acquirer processor (147).

In one embodiment, the transaction handler (103), the issuer processor(145), the acquirer processor (147), the transaction terminal (105), theportal (143), and other devices and/or services accessing the portal(143) are connected via communications networks, such as local areanetworks, cellular telecommunications networks, wireless wide areanetworks, wireless local area networks, an intranet, and Internet. Inone embodiment, dedicated communication channels are used between thetransaction handler (103) and the issuer processor (145), between thetransaction handler (103) and the acquirer processor (147), and/orbetween the portal (143) and the transaction handler (103).

In one embodiment, the transaction handler (103) uses the data warehouse(149) to store the records about the transactions, such as thetransaction records (301) or transaction data (109). In one embodiment,the transaction handler (103) includes a powerful computer, or clusterof computers functioning as a unit, controlled by instructions stored ona computer readable medium.

In one embodiment, the transaction handler (103) is configured tosupport and deliver authorization services, exception file services, andclearing and settlement services. In one embodiment, the transactionhandler (103) has a subsystem to process authorization requests andanother subsystem to perform clearing and settlement services.

In one embodiment, the transaction handler (103) is configured toprocess different types of transactions, such credit card transactions,debit card transactions, prepaid card transactions, and other types ofcommercial transactions.

In one embodiment, the transaction handler (103) facilitates thecommunications between the issuer processor (145) and the acquirerprocessor (147).

In one embodiment, the transaction terminal (105) is configured tosubmit the authorized transactions to the acquirer processor (147) forsettlement. The amount for the settlement may be different from theamount specified in the authorization request. The transaction handler(103) is coupled between the issuer processor (145) and the acquirerprocessor (147) to facilitate the clearing and settling of thetransaction. Clearing includes the exchange of financial informationbetween the issuer processor (145) and the acquirer processor (147); andsettlement includes the exchange of funds.

In one embodiment, the issuer processor (145) is to provide funds tomake payments on behalf of the consumer account (146). The acquirerprocessor (147) is to receive the funds on behalf of the merchantaccount (148). The issuer processor (145) and the acquirer processor(147) communicate with the transaction handler (103) to coordinate thetransfer of funds for the transaction. In one embodiment, the funds aretransferred electronically.

In one embodiment, the transaction terminal (105) may submit atransaction directly for settlement, without having to separately submitan authorization request.

In one embodiment, the portal (143) provides a user interface to allowthe user (101) to organize the transactions in one or more consumeraccounts (145) of the user with one or more issuers. The user (101) mayorganize the transactions using information and/or categories identifiedin the transaction records (301), such as merchant category (316),transaction date (303), amount (314), etc. Examples and techniques inone embodiment are provided in U.S. patent application Ser. No.11/378,215, filed Mar. 16, 2006, assigned Pub. No. 2007/0055597, andentitled “Method and System for Manipulating Purchase Information,” thedisclosure of which is hereby incorporated herein by reference.

In one embodiment, the portal (143) provides transaction basedstatistics, such as indicators for retail spending monitoring,indicators for merchant benchmarking, industry/market segmentation,indicators of spending patterns, etc. Further examples can be found inU.S. patent application Ser. No. 12/191,796, filed Aug. 14, 2008,assigned Pub. No. 2009/0048884, and entitled “Merchant BenchmarkingTool,” U.S. patent application Ser. No. 12/940,562, filed Nov. 5, 2010,and U.S. patent application Ser. No. 12/940,664, filed Nov. 5, 2010, thedisclosures of which applications are hereby incorporated herein byreference.

Transaction Terminal

FIG. 9 illustrates a transaction terminal according to one embodiment.In FIG. 9 , the transaction terminal (105) is configured to interactwith an account identification device (141) to obtain accountinformation (142) about the consumer account (146).

In one embodiment, the transaction terminal (105) includes a memory(167) coupled to the processor (151), which controls the operations of areader (163), an input device (153), an output device (165) and anetwork interface (161). The memory (167) may store instructions for theprocessor (151) and/or data, such as an identification that isassociated with the merchant account (148).

In one embodiment, the reader (163) includes a magnetic strip reader. Inanother embodiment, the reader (163) includes a contactless reader, suchas a radio frequency identification (RFID) reader, a near fieldcommunications (NFC) device configured to read data via magnetic fieldcoupling (in accordance with ISO standard 14443/NFC), a Bluetoothtransceiver, a WiFi transceiver, an infrared transceiver, a laserscanner, etc.

In one embodiment, the input device (153) includes key buttons that canbe used to enter the account information (142) directly into thetransaction terminal (105) without the physical presence of the accountidentification device (141). The input device (153) can be configured toprovide further information to initiate a transaction, such as apersonal identification number (PIN), password, zip code, etc. that maybe used to access the account identification device (141), or incombination with the account information (142) obtained from the accountidentification device (141).

In one embodiment, the output device (165) may include a display, aspeaker, and/or a printer to present information, such as the result ofan authorization request, a receipt for the transaction, anadvertisement, etc.

In one embodiment, the network interface (161) is configured tocommunicate with the acquirer processor (147) via a telephoneconnection, an Internet connection, or a dedicated data communicationchannel.

In one embodiment, the instructions stored in the memory (167) areconfigured at least to cause the transaction terminal (105) to send anauthorization request message to the acquirer processor (147) toinitiate a transaction. The transaction terminal (105) may or may notsend a separate request for the clearing and settling of thetransaction. The instructions stored in the memory (167) are alsoconfigured to cause the transaction terminal (105) to perform othertypes of functions discussed in this description.

In one embodiment, a transaction terminal (105) may have fewercomponents than those illustrated in FIG. 9 . For example, in oneembodiment, the transaction terminal (105) is configured for“card-not-present” transactions; and the transaction terminal (105) doesnot have a reader (163).

In one embodiment, a transaction terminal (105) may have more componentsthan those illustrated in FIG. 9 . For example, in one embodiment, thetransaction terminal (105) is an ATM machine, which includes componentsto dispense cash under certain conditions.

Account Identification Device

FIG. 10 illustrates an account identifying device according to oneembodiment. In FIG. 10 , the account identification device (141) isconfigured to carry account information (142) that identifies theconsumer account (146).

In one embodiment, the account identification device (141) includes amemory (167) coupled to the processor (151), which controls theoperations of a communication device (159), an input device (153), anaudio device (157) and a display device (155). The memory (167) maystore instructions for the processor (151) and/or data, such as theaccount information (142) associated with the consumer account (146).

In one embodiment, the account information (142) includes an identifieridentifying the issuer (and thus the issuer processor (145)) among aplurality of issuers, and an identifier identifying the consumer accountamong a plurality of consumer accounts controlled by the issuerprocessor (145). The account information (142) may include an expirationdate of the account identification device (141), the name of theconsumer holding the consumer account (146), and/or an identifieridentifying the account identification device (141) among a plurality ofaccount identification devices associated with the consumer account(146).

In one embodiment, the account information (142) may further include aloyalty program account number, accumulated rewards of the consumer inthe loyalty program, an address of the consumer, a balance of theconsumer account (146), transit information (e.g., a subway or trainpass), access information (e.g., access badges), and/or consumerinformation (e.g., name, date of birth), etc.

In one embodiment, the memory includes a nonvolatile memory, such asmagnetic strip, a memory chip, a flash memory, a Read Only Memory (ROM),etc. to store the account information (142).

In one embodiment, the information stored in the memory (167) of theaccount identification device (141) may also be in the form of datatracks that are traditionally associated with credits cards. Such tracksinclude Track 1 and Track 2. Track 1 (“International Air TransportAssociation”) stores more information than Track 2, and contains thecardholder's name as well as the account number and other discretionarydata. Track 1 is sometimes used by airlines when securing reservationswith a credit card. Track 2 (“American Banking Association”) iscurrently most commonly used and is read by ATMs and credit cardcheckers. The ABA (American Banking Association) designed thespecifications of Track 1 and banks abide by it. It contains thecardholder's account number, encrypted PIN, and other discretionarydata.

In one embodiment, the communication device (159) includes asemiconductor chip to implement a transceiver for communication with thereader (163) and an antenna to provide and/or receive wireless signals.

In one embodiment, the communication device (159) is configured tocommunicate with the reader (163). The communication device (159) mayinclude a transmitter to transmit the account information (142) viawireless transmissions, such as radio frequency signals, magneticcoupling, or infrared, Bluetooth or WiFi signals, etc.

In one embodiment, the account identification device (141) is in theform of a mobile phone, personal digital assistant (PDA), etc. The inputdevice (153) can be used to provide input to the processor (151) tocontrol the operation of the account identification device (141); andthe audio device (157) and the display device (155) may present statusinformation and/or other information, such as advertisements or offers.The account identification device (141) may include further componentsthat are not shown in FIG. 10 , such as a cellular communicationssubsystem.

In one embodiment, the communication device (159) may access the accountinformation (142) stored on the memory (167) without going through theprocessor (151).

In one embodiment, the account identification device (141) has fewercomponents than those illustrated in FIG. 10 . For example, an accountidentification device (141) does not have the input device (153), theaudio device (157) and the display device (155) in one embodiment; andin another embodiment, an account identification device (141) does nothave components (151-159).

For example, in one embodiment, an account identification device (141)is in the form of a debit card, a credit card, a smartcard, or aconsumer device that has optional features such as magnetic strips, orsmartcards.

An example of an account identification device (141) is a magnetic stripattached to a plastic substrate in the form of a card. The magneticstrip is used as the memory (167) of the account identification device(141) to provide the account information (142). Consumer information,such as account number, expiration date, and consumer name may beprinted or embossed on the card. A semiconductor chip implementing thememory (167) and the communication device (159) may also be embedded inthe plastic card to provide account information (142) in one embodiment.In one embodiment, the account identification device (141) has thesemiconductor chip but not the magnetic strip.

In one embodiment, the account identification device (141) is integratedwith a security device, such as an access card, a radio frequencyidentification (RFID) tag, a security card, a transponder, etc.

In one embodiment, the account identification device (141) is a handheldand compact device. In one embodiment, the account identification device(141) has a size suitable to be placed in a wallet or pocket of theconsumer.

Some examples of an account identification device (141) include a creditcard, a debit card, a stored value device, a payment card, a gift card,a smartcard, a smart media card, a payroll card, a health care card, awrist band, a keychain device, a supermarket discount card, atransponder, and a machine readable medium containing accountinformation (142).

Point of Interaction

In one embodiment, the point of interaction (107) is to provide anadvertisement to the user (101), or to provide information derived fromthe transaction data (109) to the user (101).

In one embodiment, an advertisement is a marketing interaction which mayinclude an announcement and/or an offer of a benefit, such as adiscount, incentive, reward, coupon, gift, cash back, or opportunity(e.g., special ticket/admission). An advertisement may include an offerof a product or service, an announcement of a product or service, or apresentation of a brand of products or services, or a notice of events,facts, opinions, etc. The advertisements can be presented in text,graphics, audio, video, or animation, and as printed matter, webcontent, interactive media, etc. An advertisement may be presented inresponse to the presence of a financial transaction card, or in responseto a financial transaction card being used to make a financialtransaction, or in response to other user activities, such as browsing aweb page, submitting a search request, communicating online, entering awireless communication zone, etc. In one embodiment, the presentation ofadvertisements may be not a result of a user action.

In one embodiment, the point of interaction (107) can be one of variousendpoints of the transaction network, such as point of sale (POS)terminals, automated teller machines (ATMs), electronic kiosks (orcomputer kiosks or interactive kiosks), self-assist checkout terminals,vending machines, gas pumps, websites of banks (e.g., issuer banks oracquirer banks of credit cards), bank statements (e.g., credit cardstatements), websites of the transaction handler (103), websites ofmerchants, checkout websites or web pages for online purchases, etc.

In one embodiment, the point of interaction (107) may be the same as thetransaction terminal (105), such as a point of sale (POS) terminal, anautomated teller machine (ATM), a mobile phone, a computer of the userfor an online transaction, etc. In one embodiment, the point ofinteraction (107) may be co-located with the transaction terminal (105),or produced by the transaction terminal (e.g., a receipt produced by thetransaction terminal (105)). In one embodiment, the point of interaction(107) may be separate from and not co-located with the transactionterminal (105), such as a mobile phone, a personal digital assistant, apersonal computer of the user, a voice mail box of the user, an emailinbox of the user, etc.

For example, the advertisements can be presented on a portion of mediafor a transaction with the customer, which portion might otherwise beunused and thus referred to as a “white space” herein. A white space canbe on a printed matter (e.g., a receipt printed for the transaction, ora printed credit card statement), on a video display (e.g., a displaymonitor of a POS terminal for a retail transaction, an ATM for cashwithdrawal or money transfer, a personal computer of the customer foronline purchases), or on an audio channel (e.g., an interactive voiceresponse (IVR) system for a transaction over a telephonic device).

In one embodiment, the white space is part of a media channel availableto present a message from the transaction handler (103) in connectionwith the processing of a transaction of the user (101). In oneembodiment, the white space is in a media channel that is used to reportinformation about a transaction of the user (101), such as anauthorization status, a confirmation message, a verification message, auser interface to verify a password for the online use of the accountinformation (142), a monthly statement, an alert or a report, or a webpage provided by the portal (143) to access a loyalty program associatedwith the consumer account (146) or a registration program.

In other embodiments, the advertisements can also be presented via othermedia channels which may not involve a transaction processed by thetransaction handler (103). For example, the advertisements can bepresented on publications or announcements (e.g., newspapers, magazines,books, directories, radio broadcasts, television, etc., which may be inan electronic form, or in a printed or painted form). The advertisementsmay be presented on paper, on websites, on billboards, or on audioportals.

In one embodiment, the transaction handler (103) purchases the rights touse the media channels from the owner or operators of the media channelsand uses the media channels as advertisement spaces. For example, whitespaces at a point of interaction (e.g., 107) with customers fortransactions processed by the transaction handler (103) can be used todeliver advertisements relevant to the customers conducting thetransactions; and the advertisement can be selected based at least inpart on the intelligence information derived from the accumulatedtransaction data (109) and/or the context at the point of interaction(107) and/or the transaction terminal (105).

In general, a point of interaction (e.g., 107) may or may not be capableof receiving inputs from the customers, and may or may not co-locatedwith a transaction terminal (e.g., 105) that initiates the transactions.The white spaces for presenting the advertisement on the point ofinteraction (107) may be on a portion of a geographical display space(e.g., on a screen), or on a temporal space (e.g., in an audio stream).

In one embodiment, the point of interaction (107) may be used toprimarily to access services not provided by the transaction handler(103), such as services provided by a search engine, a social networkingwebsite, an online marketplace, a blog, a news site, a televisionprogram provider, a radio station, a satellite, a publisher, etc.

In one embodiment, a consumer device is used as the point of interaction(107), which may be a non-portable consumer device or a portablecomputing device. The consumer device is to provide media content to theuser (101) and may receive input from the user (101).

Examples of non-portable consumer devices include a computer terminal, atelevision set, a personal computer, a set-top box, or the like.Examples of portable consumer devices include a portable computer, acellular phone, a personal digital assistant (PDA), a pager, a securitycard, a wireless terminal, or the like. The consumer device may beimplemented as a data processing system as illustrated in FIG. 11 , withmore or fewer components.

In one embodiment, the consumer device includes an accountidentification device (141). For example, a smart card used as anaccount identification device (141) is integrated with a mobile phone,or a personal digital assistant (PDA).

In one embodiment, the point of interaction (107) is integrated with atransaction terminal (105). For example, a self-service checkoutterminal includes a touch pad to interact with the user (101); and anATM machine includes a user interface subsystem to interact with theuser (101).

Hardware

In one embodiment, a computing apparatus is configured to include someof the modules or components illustrated in FIGS. 5 and 8 , such as thetransaction handler (103), the profile generator (121), the mediacontroller (115), the portal (143), the profile selector (129), theadvertisement selector (133), the user tracker (113), the correlator,and their associated storage devices, such as the data warehouse (149).

In one embodiment, at least some of the modules or componentsillustrated in FIGS. 5 and 8 , such as the transaction handler (103),the transaction terminal (105), the point of interaction (107), the usertracker (113), the media controller (115), the correlator (117), theprofile generator (121), the profile selector (129), the advertisementselector (133), the portal (143), the issuer processor (145), theacquirer processor (147), and the account identification device (141),can be implemented as a computer system, such as a data processingsystem illustrated in FIG. 11 , with more or fewer components. Some ofthe modules may share hardware or be combined on a computer system. Inone embodiment, a network of computers can be used to implement one ormore of the modules.

Further, the data illustrated in FIG. 5 , such as transaction data(109), account data (111), transaction profiles (127), and advertisementdata (135), can be stored in storage devices of one or more computersaccessible to the corresponding modules illustrated in FIG. 5 . Forexample, the transaction data (109) can be stored in the data warehouse(149) that can be implemented as a data processing system illustrated inFIG. 11 , with more or fewer components.

In one embodiment, the transaction handler (103) is a payment processingsystem, or a payment card processor, such as a card processor for creditcards, debit cards, etc.

FIG. 11 illustrates a data processing system according to oneembodiment. While FIG. 11 illustrates various components of a computersystem, it is not intended to represent any particular architecture ormanner of interconnecting the components. One embodiment may use othersystems that have fewer or more components than those shown in FIG. 11 .

In FIG. 11 , the data processing system (170) includes an inter-connect(171) (e.g., bus and system core logic), which interconnects amicroprocessor(s) (173) and memory (167). The microprocessor (173) iscoupled to cache memory (179) in the example of FIG. 11 .

In one embodiment, the inter-connect (171) interconnects themicroprocessor(s) (173) and the memory (167) together and alsointerconnects them to input/output (I/O) device(s) (175) via I/Ocontroller(s) (177). I/O devices (175) may include a display deviceand/or peripheral devices, such as mice, keyboards, modems, networkinterfaces, printers, scanners, video cameras and other devices known inthe art. In one embodiment, when the data processing system is a serversystem, some of the I/O devices (175), such as printers, scanners, mice,and/or keyboards, are optional.

In one embodiment, the inter-connect (171) includes one or more busesconnected to one another through various bridges, controllers and/oradapters. In one embodiment the I/O controllers (177) include a USB(Universal Serial Bus) adapter for controlling USB peripherals, and/oran IEEE-1394 bus adapter for controlling IEEE-1394 peripherals.

In one embodiment, the memory (167) includes one or more of: ROM (ReadOnly Memory), volatile RAM (Random Access Memory), and non-volatilememory, such as hard drive, flash memory, etc.

Volatile RAM is typically implemented as dynamic RAM (DRAM) whichrequires power continually in order to refresh or maintain the data inthe memory. Non-volatile memory is typically a magnetic hard drive, amagnetic optical drive, an optical drive (e.g., a DVD RAM), or othertype of memory system which maintains data even after power is removedfrom the system. The non-volatile memory may also be a random accessmemory.

The non-volatile memory can be a local device coupled directly to therest of the components in the data processing system. A non-volatilememory that is remote from the system, such as a network storage devicecoupled to the data processing system through a network interface suchas a modem or Ethernet interface, can also be used.

In this description, some functions and operations are described asbeing performed by or caused by software code to simplify description.However, such expressions are also used to specify that the functionsresult from execution of the code/instructions by a processor, such as amicroprocessor.

Alternatively, or in combination, the functions and operations asdescribed here can be implemented using special purpose circuitry, withor without software instructions, such as using Application-SpecificIntegrated Circuit (ASIC) or Field-Programmable Gate Array (FPGA).Embodiments can be implemented using hardwired circuitry withoutsoftware instructions, or in combination with software instructions.Thus, the techniques are limited neither to any specific combination ofhardware circuitry and software, nor to any particular source for theinstructions executed by the data processing system.

While one embodiment can be implemented in fully functioning computersand computer systems, various embodiments are capable of beingdistributed as a computing product in a variety of forms and are capableof being applied regardless of the particular type of machine orcomputer-readable media used to actually effect the distribution.

At least some aspects disclosed can be embodied, at least in part, insoftware. That is, the techniques may be carried out in a computersystem or other data processing system in response to its processor,such as a microprocessor, executing sequences of instructions containedin a memory, such as ROM, volatile RAM, non-volatile memory, cache or aremote storage device.

Routines executed to implement the embodiments may be implemented aspart of an operating system or a specific application, component,program, object, module or sequence of instructions referred to as“computer programs.” The computer programs typically include one or moreinstructions set at various times in various memory and storage devicesin a computer, and that, when read and executed by one or moreprocessors in a computer, cause the computer to perform operationsnecessary to execute elements involving the various aspects.

A machine readable medium can be used to store software and data whichwhen executed by a data processing system causes the system to performvarious methods. The executable software and data may be stored invarious places including for example ROM, volatile RAM, non-volatilememory and/or cache. Portions of this software and/or data may be storedin any one of these storage devices. Further, the data and instructionscan be obtained from centralized servers or peer to peer networks.Different portions of the data and instructions can be obtained fromdifferent centralized servers and/or peer to peer networks at differenttimes and in different communication sessions or in a same communicationsession. The data and instructions can be obtained in entirety prior tothe execution of the applications. Alternatively, portions of the dataand instructions can be obtained dynamically, just in time, when neededfor execution. Thus, it is not required that the data and instructionsbe on a machine readable medium in entirety at a particular instance oftime.

Examples of computer-readable media include but are not limited torecordable and non-recordable type media such as volatile andnon-volatile memory devices, read only memory (ROM), random accessmemory (RAM), flash memory devices, floppy and other removable disks,magnetic disk storage media, optical storage media (e.g., Compact DiskRead-Only Memory (CD ROMS), Digital Versatile Disks (DVDs), etc.), amongothers. The computer-readable media may store the instructions.

The instructions may also be embodied in digital and analogcommunication links for electrical, optical, acoustical or other formsof propagated signals, such as carrier waves, infrared signals, digitalsignals, etc. However, propagated signals, such as carrier waves,infrared signals, digital signals, etc. are not tangible machinereadable medium and are not configured to store instructions.

In general, a machine readable medium includes any mechanism thatprovides (i.e., stores and/or transmits) information in a formaccessible by a machine (e.g., a computer, network device, personaldigital assistant, manufacturing tool, any device with a set of one ormore processors, etc.).

In various embodiments, hardwired circuitry may be used in combinationwith software instructions to implement the techniques. Thus, thetechniques are neither limited to any specific combination of hardwarecircuitry and software nor to any particular source for the instructionsexecuted by the data processing system.

Other Aspects

The description and drawings are illustrative and are not to beconstrued as limiting. The present disclosure is illustrative ofinventive features to enable a person skilled in the art to make and usethe techniques. Various features, as described herein, should be used incompliance with all current and future rules, laws and regulationsrelated to privacy, security, permission, consent, authorization, andothers. Numerous specific details are described to provide a thoroughunderstanding. However, in certain instances, well known or conventionaldetails are not described in order to avoid obscuring the description.References to one or an embodiment in the present disclosure are notnecessarily references to the same embodiment; and, such references meanat least one.

The use of headings herein is merely provided for ease of reference, andshall not be interpreted in any way to limit this disclosure or thefollowing claims.

Reference to “one embodiment” or “an embodiment” means that a particularfeature, structure, or characteristic described in connection with theembodiment is included in at least one embodiment of the disclosure. Theappearances of the phrase “in one embodiment” in various places in thespecification are not necessarily all referring to the same embodiment,and are not necessarily all referring to separate or alternativeembodiments mutually exclusive of other embodiments. Moreover, variousfeatures are described which may be exhibited by one embodiment and notby others. Similarly, various requirements are described which may berequirements for one embodiment but not other embodiments. Unlessexcluded by explicit description and/or apparent incompatibility, anycombination of various features described in this description is alsoincluded here.

The disclosures of the above discussed patent documents are herebyincorporated herein by reference.

In the foregoing specification, the disclosure has been described withreference to specific exemplary embodiments thereof. It will be evidentthat various modifications may be made thereto without departing fromthe broader spirit and scope as set forth in the following claims. Thespecification and drawings are, accordingly, to be regarded in anillustrative sense rather than a restrictive sense.

What is claimed is:
 1. A method for generating and processingtransactional tokens, comprising: receiving, by a token processingsystem and from a first user device, an image of a coupon scanned by thefirst user device and an identifier associated with the first userdevice; in response to receiving the scanned image, associating, by thetoken processing system, the scanned image with the first user devicebased on the identifier of the first user device and storing, by thetoken processing system, the scanned image with the identifier in a datawarehouse, wherein the token processing system is coupled with atransaction handler and communicating with user devices, including thefirst user device associated with a first user, outside an electronicpayment processing network, wherein the transaction handlerinterconnects, as the electronic payment processing network, issuerprocessors controlling consumer accounts from which payments are madeand acquirer processors controlling merchant accounts to which thepayments are made, wherein the transaction handler is coupled with thedata warehouse storing transaction data recording the payments processedby the transaction handler in the electronic payment processing network,and account data identifying a plurality of consumer accounts;generating, by the token processing system, a first token based on thescanned image, the first token being associated with a first statecorresponding to a first benefit; associating, by the token processingsystem, the first token with the first user device based on theidentifier for the first user device and storing, by the tokenprocessing system, the first token with the identifier in the datawarehouse; monitoring, by the token processing system, usage of thefirst token in a transaction of a plurality of transactions, theplurality of transactions comprising a first transaction for which thefirst token is scanned at a first transaction terminal from the firstuser device and transmitting, by the token processing system, firsttransaction data associated with the first transaction to thetransaction handler to cause the transaction handler to apply the firstbenefit to the first transaction; determining, by the token processingsystem, that the first benefit has been applied to the first transactionby the transaction handler; in response to the first benefit beingapplied to the first transaction, updating, by the token processingsystem, the first token, such that the first token is associated with asecond state corresponding to a second benefit different from the firstbenefit; and monitoring, by the token processing system, usage of thefirst token in a second transaction for which the first token is scannedat a second transaction terminal from the first user device andtransmitting, by the token processing system, second transaction dataassociated with the second transaction to the transaction handler tocause the transaction handler to process the second transaction usingthe first token to provide the second benefit corresponding to thesecond state.
 2. The method of claim 1, wherein the first benefitcorresponds to a discount for a purchase from a first merchant, and thesecond benefit corresponds to a discount for a purchase from a secondmerchant.
 3. The method of claim 1, wherein at least one of the firstbenefit and the second benefit comprise a discount having at least oneof a fixed currency amount discount and a fixed percentage discount. 4.The method of claim 1, further comprising associating, by the tokenprocessing system, a transaction history with the first token andupdating, by the token processing system, the transaction history basedon transactions to which the first token has been applied.
 5. The methodof claim 1, wherein the first token is restricted to being used at amerchant located within a predefined geographic region or having apredefined network affiliation.
 6. The method of claim 1, furthercomprising: transferring, by the token processing system, the firsttoken from the first user device associated with the first user to asecond user device associated with a second user; and monitoring, by thetoken processing system, usage of the first token in a third transactionfor which the first token is scanned at a third transaction terminalfrom the second user device and transmitting, by the token processingsystem, third transaction data associated with the third transaction tothe transaction handler to cause the transaction handler to process thethird transaction using the first token.
 7. The method of claim 6,wherein the second user device comprises a computing device associatedwith a merchant.
 8. A system for generating and processing transactionaltokens, comprising a token processing system programmed or configuredto: receive, from a first user device, an image of a coupon scanned bythe first user device and an identifier associated with the first userdevice; in response to receiving the scanned image, associate thescanned image with the first user device based on the identifier of thefirst user device and store the scanned image with the identifier in adata warehouse, wherein the token processing system is coupled with atransaction handler and communicating with user devices, including thefirst user device associated with a first user, outside an electronicpayment processing network, wherein the transaction handlerinterconnects, as the electronic payment processing network, issuerprocessors controlling consumer accounts from which payments are madeand acquirer processors controlling merchant accounts to which thepayments are made, wherein the transaction handler is coupled with thedata warehouse storing transaction data recording the payments processedby the transaction handler in the electronic payment processing network,and account data identifying a plurality of consumer accounts; generatea first token based on the scanned image, the first token beingassociated with a first state corresponding to a first benefit;associate the first token with the first user device based on theidentifier for the first user device and store the first token with theidentifier in the data warehouse; monitor usage of the first token in atransaction of a plurality of transactions, the plurality oftransactions comprising a first transaction for which the first token isscanned at a first transaction terminal from the first user device andtransmit first transaction data associated with the first transaction tothe transaction handler to cause the transaction handler to apply thefirst benefit to the first transaction; determine that the first benefithas been applied to the first transaction by the transaction handler; inresponse to the first benefit being applied to the first transaction,update the first token, such that the first token is associated with asecond state corresponding to a second benefit different from the firstbenefit; and monitor usage of the first token in a second transactionfor which the first token is scanned at a second transaction terminalfrom the first user device and transmit second transaction dataassociated with the second transaction to the transaction handler tocause the transaction handler to process the second transaction usingthe first token to provide the second benefit corresponding to thesecond state.
 9. The system of claim 8, wherein the first benefitcorresponds to a discount for a purchase from a first merchant, and thesecond benefit corresponds to a discount for a purchase from a secondmerchant.
 10. The system of claim 8, wherein at least one of the firstbenefit and the second benefit comprise a discount having at least oneof a fixed currency amount discount and a fixed percentage discount. 11.The system of claim 8, wherein the token processing system is furtherprogrammed or configured to associate a transaction history with thefirst token and update the transaction history based on transactions towhich the first token has been applied.
 12. The system of claim 8,wherein the first token is restricted to being used at a merchantlocated within a predefined geographic region or having a predefinednetwork affiliation.
 13. The system of claim 8, wherein the tokenprocessing system is further programmed or configured to: transfer thefirst token from the first user device associated with the first user toa second user device associated with a second user; and monitor usage ofthe first token in a third transaction for which the first token isscanned at a third transaction terminal from the second user device andtransmit third transaction data associated with the third transaction tothe transaction handler to cause the transaction handler to process thethird transaction using the first token.
 14. The system of claim 13,wherein the second user device comprises a computing device associatedwith a merchant.
 15. A computer program product for generating andprocessing transactional tokens, the computer program product comprisingat least one non-transitory computer-readable medium including one ormore instructions that, when executed by at least one processor, cause atoken processing system to: receive, from a first user device, an imageof a coupon scanned by the first user device and an identifierassociated with the first user device; in response to receiving thescanned image, associate the scanned image with the first user devicebased on the identifier of the first user device and store the scannedimage with the identifier in a data warehouse, wherein the tokenprocessing system is coupled with a transaction handler andcommunicating with user devices, including the first user deviceassociated with a first user, outside an electronic payment processingnetwork, wherein the transaction handler interconnects, as theelectronic payment processing network, issuer processors controllingconsumer accounts from which payments are made and acquirer processorscontrolling merchant accounts to which the payments are made, whereinthe transaction handler is coupled with the data warehouse storingtransaction data recording the payments processed by the transactionhandler in the electronic payment processing network, and account dataidentifying a plurality of consumer accounts; generate a first tokenbased on the scanned image, the first token being associated with afirst state corresponding to a first benefit; associate the first tokenwith the first user device based on the identifier for the first userdevice and store the first token with the identifier in the datawarehouse; monitor usage of the first token in a transaction of aplurality of transactions, the plurality of transactions comprising afirst transaction for which the first token is scanned at a firsttransaction terminal from the first user device and transmit firsttransaction data associated with the first transaction to thetransaction handler to cause the transaction handler to apply the firstbenefit to the first transaction; determine that the first benefit hasbeen applied to the first transaction by the transaction handler; inresponse to the first benefit being applied to the first transaction,update the first token, such that the first token is associated with asecond state corresponding to a second benefit different from the firstbenefit; and monitor usage of the first token in a second transactionfor which the first token is scanned at a second transaction terminalfrom the first user device and transmit second transaction dataassociated with the second transaction to the transaction handler tocause the transaction handler to process the second transaction usingthe first token to provide the second benefit corresponding to thesecond state.
 16. The computer program product of claim 15, wherein thefirst benefit corresponds to a discount for a purchase from a firstmerchant, and the second benefit corresponds to a discount for apurchase from a second merchant.
 17. The computer program product ofclaim 15, wherein at least one of the first benefit and the secondbenefit comprise a discount having at least one of a fixed currencyamount discount and a fixed percentage discount.
 18. The computerprogram product of claim 15, wherein the one or more instructionsfurther cause the token processing system to associate a transactionhistory with the first token and update the transaction history based ontransactions to which the first token has been applied.
 19. The computerprogram product of claim 15, wherein the first token is restricted tobeing used at a merchant located within a predefined geographic regionor having a predefined network affiliation.
 20. The computer programproduct of claim 15, wherein the one or more instructions further causethe token processing system to: transfer the first token from the firstuser device associated with the first user to a second user deviceassociated with the second user; and monitor usage of the first token ina third transaction for which the first token is scanned at a thirdtransaction terminal from the second user device and transmit thirdtransaction data associated with the third transaction to thetransaction handler to cause the transaction handler to process thethird transaction using the first token.